Multi-parameter diabetes risk evaluations

ABSTRACT

Methods, systems and circuits evaluate a subject&#39;s risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/145,409, filed May 3, 2016, which is a continuation of U.S. patentapplication Ser. No. 13/830,784, filed Mar. 14, 2013, which claims thebenefit of and priority to U.S. Provisional Application Ser. No.61/657,315, filed Jun. 8, 2012, U.S. Provisional Application Ser. No.61/711,471, filed Oct. 9, 2012 and U.S. Provisional Application Ser. No.61/739,305, filed Dec. 19, 2012, the contents of each of which arehereby incorporated by reference as if recited in full herein.

FIELD OF THE INVENTION

The present invention relates generally to analysis of in vitrobiosamples. The invention may be particularly suitable for NMR analysisof in vitro biosamples.

BACKGROUND OF THE INVENTION

Type 2 diabetes mellitus (T2DM or “diabetes”) is one of the most costlyand burdensome chronic diseases in the U.S. and other countries. Thedefining feature of T2DM is hyperglycemia, a reflection of impairedcarbohydrate (glucose) utilization resulting from a defective ordeficient insulin secretory response. T2DM is a late manifestation ofmetabolic derangements that begin many years earlier. Its cause isbelieved to be a progressive increase in insulin resistance coupled withdeteriorating β-cell function. So long as the pancreatic β-cells areable to secrete enough insulin to compensate for the progressiveresistance of target tissues to insulin's hypoglycemic effects, thepatient is able to maintain normal fasting glucose levels. Hyperglycemiaand the transition to T2DM occur as a consequence of progressive β-celldysfunction which leads to failure to maintain hypersecretion of insulinin the face of increasing insulin resistance.

Type 2 diabetes has been traditionally diagnosed by the detection ofelevated levels of glucose (sugar) in the blood (hyperglycemia). Whilehyperglycemia defines diabetes, it is a very late stage development inthe chain of events that lead from insulin resistance to full-blowndiabetes. Accordingly, it would be desirable to have a way ofidentifying whether or not a subject is at risk for developing Type 2diabetes (i.e., is predisposed to the condition) prior to thedevelopment of the classic symptoms, such as hyperglycemia. Earlierdetection of indicators of the disease (e.g., detection before glucoselevels are elevated enough to be considered hyperglycemia) may lead tomore effective treatment of the disease, if not actual prevention of theonset of the disease.

The most direct and accurate methods for assessing insulin resistanceare laborious and time-consuming, and thus impractical for clinicalapplication. The “gold standard” among these research methods is thehyperinsulinemic euglycemic clamp, which quantifies the maximal glucosedisposal rate (GDR, inversely proportional to insulin resistance) duringthe clamp. Another arduous research method which is somewhat lessreproducible (CV 14-30%) is the frequently sampled intravenous glucosetolerance test (IVGTT) with minimal model analysis, which measuresinsulin sensitivity (S_(i)), the inverse of insulin resistance.

Risk of progression to Type 2 diabetes is currently assessed primarilyby fasting glucose, with concentrations 100-125 mg/dL defining ahigh-risk “pre-diabetes” condition and for which T2DM is currentlydefined in patients having fasting plasma glucose levels at 126 mg/dLand above. However, the actual risk of individual patients withpre-diabetes (those at risk of developing T2DM in the future) varieswidely.

NMR spectroscopy has been used to concurrently measure low densitylipoproteins (LDL), high density lipoproteins (HDL), and very lowdensity lipoproteins (VLDL), as LDL, HDL and VLDL particle subclassesfrom in vitro blood plasma or serum samples. See, U.S. Pat. Nos.4,933,844 and 6,617,167, the contents of which are hereby incorporatedby reference as if recited in full herein. U.S. Pat. No. 6,518,069 toOtvos et al. describes NMR derived measurements of glucose and/orcertain lipoprotein values to assess a patient's risk of developingT2DM.

Generally stated, to evaluate the lipoproteins in a blood plasma and/orserum sample, the amplitudes of a plurality of NMR spectroscopy derivedsignals within a chemical shift region of NMR spectra are derived bydeconvolution of the composite methyl signal envelope to yield subclassconcentrations. The subclasses are represented by many (typically over60) discrete contributing subclass signals associated with NMR frequencyand lipoprotein diameter. The NMR evaluations can interrogate the NMRsignals to produce concentrations of different subpopulations, typicallyseventy-three discrete subpopulations, 27 for VLDL, 20 for LDL and 26for HDL. These sub-populations can be further characterized asassociated with a particular size range within the VLDL, LDL or HDLsubclasses.

An advanced lipoprotein test panel, such as the LIPOPROFILE® lipoproteintest, available from LipoScience, Raleigh, N.C., has typically includeda total high density lipoprotein particle (HDL-P) measurement (e.g.,HDL-P number) that sums the concentration of all the HDL subclasses anda total low density lipoprotein particle (LDL-P) measurement that sumsthe concentration of all the LDL subclasses (e.g., LDL-P number). TheLDL-P and HDL-P numbers represent the concentration of those respectiveparticles in concentration units such as nmol/L. LipoScience has alsodeveloped a lipoprotein-based insulin resistance and sensitivity index(the “LP-IR™” index) as described in U.S. Pat. No. 8,386,187, thecontents of which are hereby incorporated by reference as if recited infull herein.

Despite the foregoing, there remains a need for evaluations that canpredict or assess a person's risk of developing type 2 diabetes beforethe onset of the disease.

SUMMARY

Embodiments of the invention provide risk assessments of a subject'srisk of having prediabetes and/or developing type-2 diabetes in thefuture using a multi-parameter (multi-variate) model of definedpredictive biomarkers.

The multi-variate risk progression model can include at least onedefined lipoprotein component, at least one defined branched chain aminoacid and at least one inflammatory biomarker.

The multi-variate model can be used for assessing patients for or duringclinical trials, during a therapy or therapies, for drug development,and/or to identify or monitor anti-obesity drugs or other drug therapycandidates.

The multi-variate model can include at least one of the following: NMRmeasurements of GlycA, valine, and a plurality of lipoprotein components(e.g., subclasses) using the same NMR spectrums.

Embodiments of the invention include, methods, circuits, NMRspectrometers or NMR analyzers, and processors that evaluate a futurerisk of developing diabetes and/or risk stratification for those having“prediabetes” by evaluating NMR spectra of an in vitro blood plasma orserum patient sample using a defined multi-component risk progressionmodel and NMR signal having a peak centered at about 2.00 ppm for GlycA.

The diabetes risk index can be calculated using a mathematical model ofrisk that generates a single score representing future risk ofdeveloping type 2 diabetes in a range from 0-100%.

The diabetes risk index can include lipoprotein components and at leastone of GlycA and valine.

The lipoprotein components can include at least one of (i) a ratio ofmedium to total high density lipoprotein particle (HDL-P) number and(ii) VLDL size.

Yet other embodiments are directed to a patient report that includes adiabetes risk index (DRI) showing a percentage from 0-100 of risk ofdiabetes conversion rate over a 1, 2, 3, 4, 5, 6, or 7 year risk windowbased on a FPG level and an associated quartile or quintile of thepatient's DRI risk score relative to a defined population. The patientreport can include the patient's risk and a comparative risk of apopulation with a lower or higher quartile or quintile DRI score and thesame FPG.

The DRI risk score can be calculated using a plurality of NMR derivedmeasurements including: lipoprotein measurements, a measure of GlycA inμmol/L and/or arbitrary units, and optionally a measure of valine inμmol/L and/or arbitrary units.

Embodiments of the invention include a method of evaluating a subject'srisk of developing type 2 diabetes and/or of having prediabetes. Themethods include programmatically calculating a diabetes risk index scoreof a subject using at least one defined mathematical model of risk ofdeveloping type 2 diabetes that includes at least one lipoproteincomponent, at least one branched chain amino acid and at least oneinflammatory biomarker obtained from at least one in vitro biosample ofthe subject.

In some embodiments, the at least one defined mathematical model of riskmay include NMR derived measurements of a plurality of selectedlipoprotein components of the at least one biosample of the subject, andNMR measurements of at least one of GlycA and valine. The definedmathematical risk model may include only NMR derived measurements of asubject's in vitro blood plasma or serum biosample.

In some embodiments, the method may include programmatically defining atleast two different mathematical models of risk of developing type 2diabetes, the at least two different mathematical models including onefor subjects on a statin therapy that includes lipoprotein componentsthat are statin insensitive and one for subjects not on a statin therapywith at least one different lipoprotein component.

In some embodiments, the method may include programmatically defining atleast two different mathematical models of risk of developing type 2diabetes. The at least two different mathematical models can havedifferent lipoprotein components including one for fasting biosamplesand one for non-fasting biosamples.

In some embodiments, the method may include programmatically generatinga report with a graph of risk of progression to type 2 diabetes in thefuture over a 1-7 year period versus ranges of fasting glucose levelsand a quartile of risk associated with the diabetes risk index score. Insome embodiments, the graph shows references of at least first (low) andfourth (high) quartile DRI scores based on a defined population tothereby allow for ease of identifying or understanding riskstratification.

In some embodiments, the method can include programmatically evaluatinga fasting blood glucose measurement of the subject using at least one invitro biosample. The diabetes risk index score can be a numerical scorewithin a defined score range, with scores associated with a fourthquartile (4Q) or fifth quintile (5Q) of a population noun reflecting anincreased or high risk of developing type 2 diabetes within 5-7 years.The method can include programmatically identifying respective subjectsthat are at increased risk of developing type 2 diabetes prior to onsetof type-2 diabetes when fasting blood glucose levels are between 90-110mg/dL and the diabetes risk score is in the 4Q or 5Q range.

In some embodiments, the method may include evaluating a fasting bloodglucose measurement of the subject, wherein the diabetes risk indexscore is a numerical score within a defined score range, with scoresassociated with a fourth quartile (4Q) or fifth quintile (5Q) of apopulation norm reflecting an increased or high risk of developing type2 diabetes within 5-7 years.

The method can include programmatically identifying respective subjectsthat are at increased risk of developing type 2 diabetes prior to onsetof type-2 diabetes when fasting blood glucose (FPG) levels are between90-125 mg/dL. The programmatically identifying can be carried out tostratify risk in subjects having the same FPG and a different diabetesrisk score.

In some embodiments, the method can include, before the programmaticcalculation, placing a blood plasma or serum sample of the subject in anNMR spectrometer; obtaining at least one NMR spectrum of the sample;deconvolving the obtained at least one NMR spectrum; and calculating NMRderived measurements of GlycA and a plurality of selected lipoproteinparameters based on the deconvolved at least one NMR spectrum. Thecalculating step may be carried out to also calculate a measurement ofbranched chain amino acid valine.

In some embodiments, the diabetes risk index score may have a definednumerical range. The method can include programmatically generating areport that identifies a respective subject as at risk of developingprediabetes if a fasting blood plasma or serum glucose value is betweenabout 90-99 mg/dl and the diabetes risk index is in a fourth quartile orfifth quintile of a population norm.

In some embodiments, the defined at least one mathematical model mayinclude NMR measurements of GlycA and a plurality of selectedlipoprotein components using lipoprotein subclasses, sizes andconcentrations measured from an in vitro blood plasma or serumbiosample.

In some embodiments, the at least one defined mathematical model may beselected lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size.

The selected lipoprotein components may include all of the listedlipoprotein components.

In some embodiments, the at least one defined mathematical model mayinclude a ratio of medium HDL-P to total HDL-P.

The at least one defined mathematical model may, in some embodiments,include VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

Before the programmatic calculation, in some embodiments, the method mayinclude electronically obtaining a composite NMR spectrum of a GlycAfitting region of the biosample of the subject, wherein the GlycAfitting region extends from 1.845 ppm to 2.080 ppm, and wherein theGlycA peak region is centered at 2.00 ppm; electronically deconvolvingthe composite NMR spectrum using a defined deconvolution model with highdensity lipoprotein (HDL) components, low density lipoprotein (LDL)components, VLDL (very low density lipoprotein)/chylomicron components,and curve fit functions associated with at least a GlycA peak region;and programmatically generating a measure of GlycA using the curve fitfunctions. The method may further include applying a conversion factorto the measure of GlycA to provide the measure in μmol/L.

In some embodiments, the curve fit functions may be overlapping curvefit functions. The measure of GlycA may be generated by summing adefined number of curve fit functions. The deconvolution model mayfurther comprise a protein signal component for protein having a densitygreater than 1.21 g/L.

Before the programmatic calculation, in some embodiments, the method mayinclude electronically obtaining NMR spectrums of a valine fittingregion of the biosample of the subject; electronically identifying avaline signal as located upstream or downstream a defined number of datapoints of a peak of a defined diluent in the biosample; electronicallydeconvolving the composite NMR spectrum using a defined deconvolutionmodel; and electronically quantifying valine using the deconvolved NMRspectrum.

In some embodiments, the at least one defined mathematical model mayinclude a plurality of different defined models, including one thatincludes lipoprotein components that are insensitive to statin therapy,one that includes lipoprotein components that are sensitive to statintherapy, one that is for fasting biosamples and one that is fornon-fasting biosamples.

Certain embodiments of the present invention are directed to a circuitconfigured to determine whether a patient is at-risk for developing type2 diabetes within the next 5-7 years and/or whether a patient hasprediabetes. The circuit includes at least one processor configured toelectronically calculate a diabetes risk index based on at least onemathematical model of risk to convergence to type 2 diabetes within 5-7years that considers at least one lipoprotein component, at least onebranched chain amino acid and GlycA from at least one in vitro biosampleof the subject.

The at least one mathematical model of risk, in some embodiments, mayinclude NMR derived measurements of GlycA, valine and a plurality oflipoprotein components.

In some embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes. The at least two different mathematical models caninclude a first model for subjects on a statin therapy that includeslipoprotein components that are statin insensitive and a second modelfor subjects not on a statin therapy. The second model can include atleast some lipoprotein components that are different from the firstmodel. The circuit can be configured to identify subject characteristicsto select the appropriate first or second model of risk for thecalculation of the diabetes risk index score.

In certain embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes with different lipoprotein components, the at least twodifferent mathematical models including one for fasting biosamples, andone for non-fasting biosamples. The circuit can identify subjectcharacteristics to select the appropriate mathematical model of risk forcalculation of the diabetes risk index score.

In some embodiments, the at least one processor may be configured togenerate a report with a graph of risk of progression to type 2 diabetesin the future over a 1-7 year period versus ranges of fasting glucoselevels and a quartile of risk associated with the diabetes risk indexscore. The graph may include visual references of at least first (low)and fourth (high) quartile DRI scores based on a defined population tothereby allow for ease of identifying or understanding riskstratification.

The at least one processor, in some embodiments, may be configured toevaluate a fasting blood glucose measurement of the subject, wherein thediabetes risk index score is a numerical score within a defined scorerange, with scores associated with a fourth quartile (4Q) or fifthquintile (5Q) of a population norm reflecting an increased or high riskof developing type 2 diabetes within 5-7 years. The at least oneprocessor can be configured to identify respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose levels are between 90-110 mg/dL andthe diabetes risk score is in the 4Q or 5Q range.

In some embodiments, the at least one processor may be configured toevaluate a fasting blood glucose measurement of the subject. Thediabetes risk index score can be a numerical score within a definedscore range, with scores associated with a fourth quartile (4Q) or fifthquintile (5Q) of a population norm reflecting an increased or high riskof developing type 2 diabetes within 5-7 years. The at least oneprocessor can be configured to identify respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose (FPG) levels are between 90-125mg/dL. The at least one processor is configured to generate a reportthat can stratify risk in subjects having the same FPG and a differentdiabetes risk score.

In some embodiments, the at least one mathematical model may include aplurality of lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The mathematical model mayinclude all of the listed lipoprotein components.

In some embodiments, one of the lipoprotein components of themathematical model may be a ratio of medium HDL-P to total HDL-P.

In certain embodiments, the at least one mathematical model may includea plurality of lipoprotein components including VLDL subclass particlesize (vsz3), a ratio of medium HDL-P to total HDL-P (HMP_HDLP)multiplied by GlycA and a ratio of VLDL size by a sum of large VLDL-Pand medium VLDL-P.

Certain embodiments of the present invention are directed to a computerprogram product for evaluating in vitro patient biosamples. The computerprogram product includes a non-transitory computer readable storagemedium having computer readable program code embodied in the medium. Thecomputer-readable program code includes computer readable program codethat provides at least one mathematical model of risk to progression totype 2 diabetes over a defined time period of between 1-7 years. The atleast one mathematical model of risk to progression to type 2 diabetescan include a plurality of components, including at least onelipoprotein component, at least one inflammatory marker and at least onebranched chain amino acid; and computer readable program code thatcalculates a diabetes risk index associated with a patient's biosamplebased on the at least one mathematical model of a risk of developingtype 2 diabetes.

In some embodiments, the computer readable program code that providesthe at least one mathematical model may include model components of NMRderived measurements of GlycA and valine.

In some embodiments, the computer program product may include computerreadable program code configured to evaluate a glucose measurement ofthe patient. The computer readable program code that calculates adiabetes risk index can calculate the index as a numerical score withina defined score range, with scores associated with a fourth quartile(4Q) or fifth quintile (5Q) of a population noun reflecting an increasedor high risk of developing type 2 diabetes within 5-7 years.

The computer program product may further include computer readableprogram code configured to identify respective patients that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose levels are between 90-110 mg/dL andthe diabetes risk score is in the 4Q or 5Q range.

In some embodiments, the computer program product may be configured togenerate a report that identifies respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type-2diabetes when fasting blood glucose (FPG) levels are between 90-125mg/dL. The mathematical model can stratify risk in subjects having thesame FPG and a different diabetes risk score.

In some embodiments, the at least one mathematical model may include aplurality of lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The mathematical model mayinclude all of the listed lipoprotein components.

In some embodiments, the mathematical model may include a ratio ofmedium HDL-P to total HDL-P. In some embodiments, the at least onemathematical model may include a plurality of lipoprotein componentsincluding VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

The computer program product may further include computer readableprogram code that identifies and deconvolves a valine fitting region ofa composite NMR spectrum blood serum or plasma sample of a subject andgenerates a calculated measurement of valine; and computer readableprogram code that deconvolves a GlycA fitting region of the compositeNMR spectrum. The computer readable program code can deconvolve thecomposite NMR spectrum using a defined GlycA deconvolution model with(i) high density lipoprotein (HDL) components, (ii) low densitylipoprotein (LDL) components, (iii) VLDL (very low densitylipoprotein)/chylomicron components, (iv) another defined protein signalcomponent and (v) curve fitting functions applied to at least a GlycApeak region and generates a calculated measurement of GlycA.

Still other embodiments are directed to a system. The system includes anNMR spectrometer for acquiring at least one NMR spectrum of an in vitrobiosample and at least one processor in communication with the NMRspectrometer. The at least one processor is configured to determine fora respective biosample using the at least one NMR spectrum a diabetesrisk index score based on at least one defined mathematical model ofrisk to convergence to type 2 diabetes within 5-7 years that considersat least one lipoprotein component, at least one branched chain aminoacid and at least one inflammatory biomarker obtained from at least onein vitro biosample of the subject.

The at least one processor may be configured to deconvolve the at leastone NMR spectrum and generate: (i) an NMR measurement of GlycA: (ii) anNMR measurement of valine; (iii) NMR measurements of lipoproteinparameters; and (iv) the diabetes risk index using the NMR measurementsof GlycA, valine as components of the at least one defined mathematicalmodel.

In some embodiments, the at least one processor may be configured todefine at least two different mathematical models of risk of developingtype 2 diabetes. The at least two different mathematical models caninclude one for subjects on a statin therapy that includes lipoproteincomponents that are statin insensitive and one for subjects not on astatin therapy with different lipoprotein components.

In some embodiments, the at least one processor in the system may beconfigured to define at least two different mathematical models of riskof developing type 2 diabetes with different lipoprotein components, theat least two different mathematical models including one for fastingbiosamples, and one for non-fasting biosamples.

The at least one processor in the system, in some embodiments, may beconfigured to generate a report with a graph of risk of progression totype 2 diabetes in the future over a 1-7 year period versus ranges offasting glucose levels and a quartile of risk associated with thediabetes risk index score. The graph may include visual references of atleast first (low) and fourth (high) quartile DRI scores based on adefined population to thereby allow for ease of identifying orunderstanding risk stratification.

In some embodiments, the defined at least one mathematical model mayinclude NMR measurements of GlycA and a plurality of selectedlipoprotein components using lipoprotein subclasses, sizes andconcentrations measured from an in vitro blood plasma or serumbiosample.

In some embodiments, the at least one defined mathematical model mayinclude selected lipoprotein components comprising at least two of thefollowing: large VLDL subclass particle number, medium VLDL subclassparticle number, total HDL subclass particle number, medium HDL subclassparticle number and VLDL particle size. The selected lipoproteincomponents may include all of the listed lipoprotein components.

In some embodiments, the at least one defined mathematical model mayinclude a ratio of medium HDL-P to total HDL-P.

In some embodiments, the at least one defined mathematical model mayinclude VLDL subclass particle size (vsz3), a ratio of medium HDL-P tototal HDL-P (HMP_HDLP) multiplied by GlycA and a ratio of VLDL size by asum of large VLDL-P and medium VLDL-P.

Other embodiments of the present invention are directed to a patientreport comprising: a diabetes risk index score calculated based on adefined mathematical model of risk of progression to type 2 diabeteswithin the next 5-7 years, with values above a population normassociated with increased risk, and comprising a graph showing apercentage in a range of risk of diabetes conversion over a 1, 2, 3, 4,5, 6, or 7 year risk window versus glucose level and an associatedquartile or quintile of the patient's DRI risk score relative to adefined population, and optionally a comparative risk of a populationwith a lower or higher quartile or quintile DRI score and the sameglucose measurement.

Still other embodiments are directed to NMR systems. The systems includea NMR spectrometer; a flow probe in communication with the spectrometer;and at least one processor in communication with the spectrometer. Theat least one processor is configured to: (a) obtain (i) NMR signal of adefined GlycA fitting region of NMR spectra associated with GlycA of ablood plasma or serum specimen in the flow probe; (ii) NMR signal of adefined valine fitting region of NMR spectra associated with thespecimen in the flow probe; and (iii) NMR signal of lipoproteinparameters; (b) calculate measurements of GlycA, valine and thelipoprotein parameters; and (c) calculate a diabetes risk index using adefined mathematical model of risk of developing type 2 diabetes and/orhaving prediabetes that uses the calculated measurements of GlycA,valine and at least a plurality of the lipoprotein parameters.

The at least one processor in the NMR system may include at least onelocal or remote processor the NMR analyzer, wherein the at least oneprocessor is configured to (i) deconvolve at least one composite NMRspectrum of the specimen to generate a measurement of GlycA, valine andthe lipoprotein parameters.

Additional aspects of the present invention are directed to methods ofmonitoring a patient to evaluate a therapy or determine whether thepatient is at-risk of developing type 2 diabetes or has prediabetes. Themethods include: programmatically providing at least one definedmathematical model of risk of progression to type 2 diabetes thatincludes a plurality of components including NMR derived measurements ofselected lipoprotein subclasses and at least one of valine or GlycA;programmatically deconvolving at least one NMR spectrum of respective invitro patient blood plasma or serum samples and determining measurementsof lipoprotein subclasses, GlycA and valine; programmaticallycalculating a diabetes risk index score of the respective patients usingthe at least one defined model and corresponding patient samplemeasurements; and evaluating at least one of (i) whether the diabetesrisk index is above a defined level of a population norm associated withincreased risk of developing type 2 diabetes; or (ii) whether thediabetes risk index is increasing or decreasing over time in response toa therapy.

Further features, advantages and details of the present invention willbe appreciated by those of ordinary skill in the art from a reading ofthe figures and the detailed description of the preferred embodimentsthat follow, such description being merely illustrative of the presentinvention. Features described with respect with one embodiment can beincorporated with other embodiments although not specifically discussedtherewith. That is, it is noted that aspects of the invention describedwith respect to one embodiment, may be incorporated in a differentembodiment although not specifically described relative thereto. Thatis, all embodiments and/or features of any embodiment can be combined inany way and/or combination. Applicant reserves the right to change anyoriginally filed claim or file any new claim accordingly, including theright to be able to amend any originally filed claim to depend fromand/or incorporate any feature of any other claim although notoriginally claimed in that manner. The foregoing and other aspects ofthe present invention are explained in detail in the specification setforth below.

As will be appreciated by those of skill in the art in light of thepresent disclosure, embodiments of the present invention may includemethods, systems, apparatus and/or computer program products orcombinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a graph showing a 5 year conversion risk of developing T2DMbased on fasting glucose levels (mg/dl) and first and fourth quartiles(Q1, Q4, respectively) of a diabetes risk index according to embodimentsof the present invention. The data in the table represent the percent ofsubjects in the 1^(st) and 4^(th) quartiles of six glucose subgroupsthat convert to type 2 diabetes over a 5 year period.

FIG. 2 is an NMR spectrum showing inflammation markers in the plasma NMRspectrum (N-acetyl methyl signals from glycosylated acute phaseproteins) associated with defined NMR markers, GlycA and GlycB,respectively, according to embodiments of the present invention.

FIG. 3A is an example of a fitting function/deconvolution model thatuses four valine (quartet) signals to calculate NMR measures of valineaccording to embodiments of the present invention.

FIG. 3B is an expansion of the plasma NMR spectrum containing methylsignals from lipoproteins and branched-chain amino acids according toembodiments of the present invention.

FIG. 3C shows the full NMR spectrum containing methyl signals fromlipoproteins and branched-chain amino acids with an expansion showingthe location of signals from the noted metabolites according toembodiments of the present invention.

FIG. 4A is an NMR spectrum showing glucose signal as multiplets atseveral locations according to embodiments of the present invention.

FIG. 4B is a region of the blood plasma proton NMR spectrum containingglucose peaks according to embodiments of the present invention.

FIGS. 5A and 5B are schematic illustrations of the chemical structuresof the carbohydrate portion of N-acetylglycosylated proteins showing theCH3 group that gives rise to the GlycA NMR signal.

FIGS. 6A and 6B are schematic illustrations of the chemical structuresof the carbohydrate portion of N-acetylneuraminic acid modifiedglycoproteins showing the CH3 group that gives rise to the GlycB NMRsignal.

FIG. 7A is a graph showing an expanded section of the plasma NMRspectrum containing the signal envelope from the plasma lipoproteins andthe underlying GlycA and GlycB signals according to embodiments of thepresent invention.

FIGS. 7B and 7C are graphs of the NMR spectral region shown in FIG. 7Aillustrating deconvolution models to yield NMR signal for measurement ofGlycA and GlycB according to embodiments of the present invention.

FIG. 7D is a table of different components in a GlycA/B deconvolutionmodel according to embodiments of the present invention.

FIG. 7E is an NMR spectrum showing metabolite A present in a sample attypical normal (low) concentration according to embodiments of thepresent invention.

FIG. 7F is an NMR spectrum showing metabolite A present in a sample atan elevated (high) concentration according to embodiments of the presentinvention.

FIGS. 8A-8D are graphs of the GlycA NMR spectral region illustratingspectral overlap from lipoprotein signals (particularly fromVLDL/Chylos) for samples with high TG (triglycerides).

FIG. 9A is a table of different measures of GlycA concentration,depending on a protein component used in the deconvolution (e.g.,“fitting”) model.

FIGS. 9B-9D illustrate the GlycA and GlycB “fits” (deconvolution) of thesame plasma sample using deconvolution models with different proteincomponents (#1-#3 in the table in FIG. 9A) according to embodiments ofthe present invention.

FIG. 10 is a schematic screen shot of the deconvolution of a 10 mmol/Lreference sample of N-acetylglucosamine, used to generate a conversionfactor relating GlycA and GlycB signal areas to glycoprotein N-acetylmethyl group concentrations according to embodiments of the presentinvention.

FIGS. 11A and 11B are schematic illustrations of different lipoproteinsubpopulations (subclasses) according to embodiments of the presentinvention.

FIG. 12A is a flow diagram of an NMR valine test protocol according toembodiments of the present invention.

FIG. 12B is a flow chart of exemplary pre-analytical processing that canbe used prior to obtaining NMR signal of biosamples according toembodiments of the present invention.

FIG. 12C is a flow diagram of operations that can be used to evaluatevaline using NMR according to embodiments of the present invention.

FIG. 13 is a chart of prospective associations of hs-CRP andNMR-measured GlycA and NMR-measured valine levels with various diseaseoutcomes in MESA (n=5680) according to embodiments of the presentinvention.

FIG. 14 is a chart of characteristics of MESA subjects by NMR measuredGlycA quartile (in “NMR signal area units”) according to embodiments ofthe present invention.

FIG. 15 is a schematic illustration of a system for analyzing apatient's predictable risk using a DRI risk index module and/or circuitusing according to embodiments of the present invention.

FIG. 16 is a schematic illustration of a NMR spectroscopy apparatusaccording to embodiments of the present invention.

FIG. 17 is a schematic diagram of a data processing system according toembodiments of the present invention.

FIG. 18 is a flow chart of exemplary operations that can be used toassess risk of developing T2DM according to embodiments of the presentinvention.

FIG. 19 is a flow chart of exemplary operations that can be used toassess a risk of developing T2DM in the future and/or havingprediabetes, according to embodiments of the present invention.

FIG. 20A is an example of a patient report that includes a GlycAmeasurement and/or a diabetes risk index according to embodiments of thepresent invention.

FIG. 20B is another example of a patient report with a visual (typicallycolor-coded) graphic summary of a continuum of risk from low to highaccording to embodiments of the present invention.

FIG. 21 is a prophetic example of a graph of DRI versus time that can beused to monitor change to evaluate a patient's risk status, change instatus, and/or clinical efficacy of a therapy or even used for clinicaltrials or to contradict planned therapies and the like according toembodiments of the present invention.

FIGS. 22A and 22B are graphical patient/clinical reports of % risk ofdiabetes versus FPG level and DRI score and risk pathway. FIG. 22A showspatient #1's score while FIG. 22B shows patient #1's score in comparisonwith a lesser risk patient (patient number 2) having the same FPG. Whileeach patient has the same FPG, they have different metabolic issuesidentified by the DRI scores stratifying risk according to embodimentsof the present invention.

FIGS. 23A-23C are graphical patient/clinical reports of diabetesconversion rate (%) versus FPG level and DRI score (high DRI, Q4 and lowDRI, Q1) according to embodiments of the present invention. FIG. 23A isfor a 4-year risk of conversion to diabetes. FIG. 23B is a 5-year riskof conversion and FIG. 23C is a 6 year risk of conversion.

FIG. 24 is a graphical patient/clinical report of a Q4/Q1 relative riskof diabetes conversion (1-8) versus FPG level and DRI score for both6-year (upper line) and 2-year conversions periods according toembodiments of the present invention.

FIG. 25 is a graphical patient/clinical report of log scale 5 yearconversion with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 26 is a graphical patient/clinical report of 5 year conversion todiabetes with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 27 is a table of data that show the performance of DRI in the IRASdataset, the MESA dataset, and IRAS dataset (using the glucose subgroupsfrom MESA) according to embodiments of the present invention.

FIG. 28 shows the performance of DRI (w/o glucose) in the same datasetcriteria as FIG. 27 according to embodiments of the present invention.

The foregoing and other objects and aspects of the present invention areexplained in detail in the specification set forth below.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions, layersand/or sections, these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are only usedto distinguish one element, component, region, layer or section fromanother region, layer or section. Thus, a first element, component,region, layer or section discussed below could be termed a secondelement, component, region, layer or section without departing from theteachings of the present invention. The sequence of operations (orsteps) is not limited to the order presented in the claims or figuresunless specifically indicated otherwise.

The term “programmatically” means carried out using computer programand/or software, processor or ASIC directed operations. The term“electronic” and derivatives thereof refer to automated orsemi-automated operations carried out using devices with electricalcircuits and/or modules rather than via mental steps and typicallyrefers to operations that are carried out programmatically. The terms“automated” and “automatic” means that the operations can be carried outwith minimal or no manual labor or input. The term “semi-automated”refers to allowing operators some input or activation, but thecalculations and signal acquisition as well as the calculation of theconcentrations of the ionized constituent(s) are done electronically,typically programmatically, without requiring manual input.

The term “about” refers to +/−10% (mean or average) of a specified valueor number.

The term “prediabetes” refers to a patient or subject that has not beendiagnosed with type 2 diabetes and, as currently defined by the AmericanDiabetes Association, is associated with individuals that have a fastingplasma glucose level that is between 100 and 125 mg/dL, an oral glucosetolerance test level that is between 140-199 (mg/dL) or an A1C percentthat is between 5.7 to 6.4 as represented in Table 1 below (the greaterthe level, the higher the risk of type 2 diabetes for each type of test.

TABLE 1 Prediabetes and Diabetes Levels Blood Test Levels for Diagnosisof Diabetes and Prediabetes Oral Glucose A1C Fasting Plasma ToleranceTest (percent) Glucose (mg/dL) (mb/dL Diabetes 6.5 or above 126 or above200 or above Prediabetes 5.7 to 6.4 100 to 125 140 to 199 Normal About 5 99 or below 139 or below Definitions: mg = milligram, dL = deciliterFor all three tests, within the prediabetes range, the higher the testresult, the greater the risk of diabetes

See, American Diabetes Association. Standards of medical care indiabetes 2012. Diabetes Care. 2012:35 (Supp 1):S12, table 2. It iscontemplated by embodiments of the invention that the use of a diabetesrisk score can be used alone or with FPG to diagnose an individual ashaving prediabetes and can stratify risk for patients having the sameFGP but different metabolic issues.

Embodiments of the invention can evaluate a patient's risk of havingtype 2 diabetes within a 5-7 time frame, typically within about a 5 yeartime frame as a model of conversion to this condition and may alsoevaluate whether the patient has prediabetes, impaired fasting glucoseor impaired glucose tolerance.

The term “patient” is used broadly and refers to an individual thatprovides a biosample for testing or analysis.

The term “GlycA” refers to a new biomarker that is derived from ameasure of composite NMR signal from carbohydrate portions of acutephase reactant glycoproteins containing N-acetylglucosamine and/orN-acetylgalactosamine moieties, more particularly from the protons ofthe 2-NAcGlc and 2-NAcGal methyl groups. The GlycA signal is centered atabout 2.00 ppm in a plasma NMR spectrum at about 47 degrees C. (+/−0.5degrees C.). The peak location is independent of spectrometer field butmay vary depending on analysis temperature of the biosample and is notfound in urine biosamples. Thus, the GlycA peak region may vary if thetemperature of the test sample varies. The GlycA NMR signal may includea subset of NMR signal at the defined peak region so as to include onlyclinically relevant signal contributions and may exclude a proteincontribution to the signal in this region as will be discussed furtherbelow.

The term “GlycB” refers to a new biomarker that is derived from ameasure of composite NMR signal from the carbohydrate portions of acutephase reactant glycoproteins containing N-acetylneuraminic acid (sialicacid) moieties, more particularly from the protons of the 5-Nacetylmethyl groups. The GlycB signal is centered at about 2.04 ppm in theplasma NMR spectrum at about 47 degrees C. The peak location isindependent of spectrometer field but may vary depending on analysistemperature of the biosample. Thus, the GlycB peak region may vary ifthe temperature of the test sample varies (and is not found in urinesamples).

As used herein, the chemical shift locations (ppm) refer to NMR spectrareferenced internally to CaEDTA signal at 2.519 ppm. Thus, the notedpeak locations discussed and/or claimed herein may vary depending on howthe chemical shift is generated or referenced as is well known to thoseof skill in the art. Thus, to be clear, certain of the described and/orclaimed peak locations have equivalent different peak locations in othercorresponding chemical shifts as is well known to those of skill in theart.

The term “biosample” refers to in vitro blood, plasma, serum, CSF,lavage, sputum, or tissue samples of humans or animals. Embodiments ofthe invention may be particularly suitable for evaluating human bloodplasma or serum biosamples, particularly for GlycA and GlycB (which arenot found in urine, for example). The blood plasma or serum samples maybe fasting or non-fasting. Where glucose is measured by NMR, thebiosample is typically fasting blood plasma or serum samples.

The terms “population norm” and “standard” refer to values defined by alarge study or studies such as the Framingham Offspring Study or theMulti-Ethnic Study of Atherosclerosis (MESA) or other study having alarge enough sample to be representative of the general population.However, the instant invention is not limited to the population valuesin MESA as the presently defined normal and at-risk population values orlevels may change over time. Thus, a reference range associated withvalues from a defined population in risk segments (e.g., quartiles orquintiles) can be provided and used to assess elevated or reduced levelsand/or risk of having a clinical disease state.

The term “clinical disease state” means an at-risk medical conditionsuch as prediabetes, that may indicate medical intervention, therapy,therapy adjustment or exclusion of a certain therapy (e.g.,pharmaceutical drug) and/or monitoring is appropriate. Identification ofa likelihood of a clinical disease such as prediabetes can allow aclinician to treat, delay or inhibit onset of the condition accordingly.

As used herein, the term “NMR spectral analysis” means using proton (¹H)nuclear magnetic resonance spectroscopy techniques to obtain data thatcan measure the respective parameters present in the biosample, e.g.,blood plasma or blood serum. “Measuring” and derivatives thereof refersto determining a level or concentration and/or for certain lipoproteinsubclasses, measuring the average particle size thereof. The term “NMRderived” means that the associated measurement is calculated using NMRsignal/spectra from one or more scans of an in vitro biosample in an NMRspectrometer.

The term “downfield” refers to a region/location on the NMR spectrumthat pertains to the left of a certain peak/location/point (higher ppmscale relative to a reference). Conversely, the term “upfield” refers toa region/location on the NMR spectrum that pertains to the right of acertain peak/location/point.

The terms “mathematical model” and “model” are used interchangeably andwhen used with “DRI”, “diabetes risk index”, or “risk” refer to astatistical model of risk used to evaluate a subject's risk ofdeveloping type 2 diabetes in the future, typically within 5-7 years.The risk model can be or include any suitable model including, but notlimited to, one or more of a logistic model, a mixed model or ahierarchical linear model. The risk model can provide a measure of riskbased on the probability of conversion to type 2 diabetes within adefined time frame, typically within 5-7 years.

The term “LP-IR™” score refers to an insulin resistance score that ratesa subject's insulin sensitivity from insulin sensitive to insulinresistant using a summation of risk scores associated with differentdefined lipoprotein components. See, e.g., U.S. Pat. No. 8,386,187 for adetailed discussion of the LP-IR score, the contents of which are herebyincorporated by reference as if recited in full herein.

The term “lipoprotein component” refers to a component in themathematical risk model associated with lipoprotein particles includingsize and/or concentration of one or more subclasses (subtypes) oflipoproteins. Lipoprotein components can include any of the lipoproteinparticle subclasses, concentrations, sizes, ratios and/or mathematicalproducts (multiplied) of lipoprotein parameters and/or lipoproteinsubclass measurements of defined lipoprotein parameters combined withother parameters.

The DRI mathematical model can use other clinical parameters such asgender, age, BMI, whether on hypertension medicine and the like.

Lipoproteins include a wide variety of particles found in plasma, serum,whole blood, and lymph, comprising various types and quantities oftriglycerides, cholesterol, phospholipids, sphyngolipids, and proteins.These various particles permit the solublization of otherwisehydrophobic lipid molecules in blood and serve a variety of functionsrelated to lipolysis, lipogenesis, and lipid transport between the gut,liver, muscle tissue and adipose tissue. In blood and/or plasma,lipoproteins have been classified in many ways, generally based onphysical properties such as density or electrophoretic mobility.

Classification based on nuclear magnetic resonance-determined particlesize distinguishes distinct lipoprotein particles based on size or sizeranges. For example, the NMR measurements can identify at least 15distinct lipoprotein particle subtypes, including at least 5 subtypes ofhigh density lipoproteins (HDL), at least 4 subtypes of low densitylipoproteins (LDL), and at least 6 subtypes of very low densitylipoproteins (VLDL), which can be designated TRL (triglyceride richlipoprotein) V1 through V6. As shown in FIG. 11A, current analysismethodology allows NMR measurements that can provide concentrations of73 subpopulations with 27 VLDL, 20 LDL and 26 HDL subpopulations toproduce measurements of groups of defined small and large subpopulationsof respective groups.

It is also noted that while NMR measurements of the lipoproteinparticles are contemplated as being particularly suitable for theanalyses described herein, it is contemplated that other technologiesmay be used to measure these parameters now or in the future andembodiments of the invention are not limited to this measurementmethodology. For example, flotation and ultracentrifugtion employ adensity-based separation technique for evaluating lipoprotein particles.Ion mobility analysis is a different technology for measuringlipoprotein subclasses.

FIG. 11B illustrates examples of lipoprotein subclass groupings,including those with concentrations that can be summed to determineHDL-P and LDL-P numbers according to some particular embodiments of thepresent invention. Embodiments of the invention classify lipoproteinparticles into subclasses grouped by size ranges based onfunctional/metabolic relatedness as assessed by their correlations withlipid and metabolic variables. Thus, as noted above, the evaluations canmeasure over 20 discrete subpopulations (sizes) of lipoproteinparticles, typically between about 30-80 different size subpopulations(or even more). FIG. 11B also shows these discrete sub-populations canbe grouped into defined subclasses, including three each for VLDL andHDL and two or three for LDL (if the former, with one of the threeidentified as IDL in the size range between large LDL and small VLDL).

For the GlycA and/or GlycB measurement calculations, the discrete numberof HDL and LDL groupings can be less than those used to quantitativelymeasure the lipoprotein subclasses. The subclasses of different size canbe quantified from the amplitudes of their spectroscopically distinctlipid methyl group NMR signals. See, Jeyarajah et al., Lipoproteinparticle analysis by nuclear magnetic resonance spectroscopy, Clin LabMed. 2006; 26: pp. 847-870, the contents of which are herebyincorporated by reference as if recited in full herein. The NMR derivedHDL-P and LDL-P particle sizes noted herein typically refer to averagemeasurements, but other size demarcations may be used.

The term “LDL-P” refers to a low density lipoprotein particle number(LDL-P) measurement (e.g., LDL-P number) that sums the concentration ofdefined LDL subclasses. Total LDL-P can be generated using a total lowdensity lipoprotein particle (LDL-P) measurement that sums theconcentration (μmol/L) of all the LDL subclasses (large and small)including sizes between 18-23 nm. In some embodiments, the LDL-Pmeasurement may employ selected combinations of the LDL subclasses(rather than the total of all LDL subclass subpopulations). As usedherein, the term “small LDL particles” typically includes particleswhose sizes range from between about 18 to less than 20.5 nm, typicallybetween 19-20 nm. The term “large LDL particles” includes particlesranging in diameter from between about 20.5-23 nm. It is noted that theLDL subclasses of particles can be divided in other size ranges. Forexample, the “small” size may be between about 19-20.5 nm, intermediatemay be between about 20.5-21.2 nm, and large may be between about21.2-23 nm. In addition, intermediate-density lipoprotein particles(“IDL” or “IDL-P”), which range in diameter from between about 23-29 nm,can be included among the particles defined as “large” LDL (or evensmall VLDL). Thus, for example, the LDL subclasses can be between 19-28nm.

The term “HDL-P” refers to a high density lipoprotein particle number(HDL-P) measurement (e.g., HDL-P number) that sums the concentration ofdefined HDL subclasses. Total HDL-P can be generated using a total highdensity lipoprotein particle (HDL-P) measurement that sums theconcentration (μmol/L) of all the HDL subclasses (large, medium andsmall) in the size range between about 7 nm (on average) to about 14 rim(on average), typically between 7.4-13.5 nm. In some embodiments, theHDL-P measurement may employ selected combinations of the HDL subclasses(rather than all subpopulations of HDL subclasses). The term “large HDLparticles” (“large HDL-P”) typically includes HDL subclasses ofparticles whose sizes range from between about 9.4 to about 14 nm, andmay be defined for particles with sizes between 9.7-13.5 nm. The term“small HDL particles” (small HDL-P) typically includes particles rangingin diameter between about 7 (typically about 7.3 or 7.4 nm) to about 8.2nm. The intermediate or medium HDL particles (medium HDL-P) can beparsed into one of the small or large designations or be measuredseparately as including particles in the size range that is typicallybetween about 8.2 to 9.4 nm, such as 8.3-9.4 nm. Thus, either or boththe ranges of size above can be broadened to include some or all thesizes of the intermediate HDL particles.

HDL subclass particles typically range (on average) from between about 7nm to about 15 nm, more typically about 7.3 nm to about 14 nm (e.g., 7.4nm-13.5 nm). The HDL-P concentration is the sum of the particleconcentrations of the respective subpopulations of its HDL-subclasses,e.g., small HDL-P can include H1-H8, H1-H9, H1-H10 or H1-H11subpopulations, for example.

The different subpopulations of HDL-P can be identified by a number from1-26, with “1” representing the lowest size subpopulation in the HDLsubclass and “26” being the largest size subpopulation in the HDLsubclass. The small HDL components can include H1-H8, or H1-H11 and thelarge HDL components including H16-H26.

The term “large VLDL particles” refers to particles at or above 60 nmsuch as between 60-260 nm. The term “medium VLDL particles” refers toparticles in sizes between 35-60 nm. The term “small VLDL particles”refers to particles between 29-35 nm. The term “VLDL-P” refers to a verylow density lipoprotein particle number (VLDL-P) measurement (e.g.,VLDL-P number) that sums the concentration of defined VLDL subclasses.Total VLDL-P can be generated using a total very low density lipoproteinparticle (HDL-P) measurement that sums the concentration (μmol/L) of allthe VLDL subclasses (large, medium and small).

It is noted that the “small, large and medium” size ranges noted abovecan vary or be redefined to widen or narrow the upper or lower endvalues thereof. The particle sizes noted above typically refer toaverage measurements, but other demarcations may be used.

As is known to those of skill in the art, valine is an α-amino acid withthe chemical formula HO₂CCH(NH₂)CH(CH₃)₂. When measured by NMR, thevalue can be unitless. The valine measurement may be multiplied by adefined conversion factor to convert the value into concentration units.

Inflammation can be associated with many different disease statesincluding, but not limited to prediabetes, T2DM and CHD. It is alsobelieved that inflammation may modulate HDL functionality. See, e.g.,Fogelman, When Good Cholesterol Goes Bad, Nature Medicine, 2004.Carbohydrate components of glycoproteins can perform biologicalfunctions in protein sorting, immune and receptor recognition,inflammation and other cellular processes.

Generally stated, embodiments of the present invention provide at leastone Diabetes Risk Index (DRI) using one or more defined mathematicalmodels of risk of different defined biomarkers or parameters of an invitro biosample of a patient to identify at-risk patients before onsetof T2DM who may benefit from pharmaceutical, medical, diet, exercise orother intervention.

The DRI model(s) can include at least one inflammatory marker, at leastone lipoprotein component and at least one other defined metabolite orbiomarker.

In preferred embodiments, the DRI risk progression model parameters caninclude NMR derived measurements of deconvolved signal associated with acommon NMR spectrum of lipoproteins using defined deconvolution modelsthat characterize deconvolution components for protein and lipoproteins,including HDL, LDL, VLDL/chylos. This type of analysis can provide for arapid scan acquisition time of under 1 minute, typically between about20 s-40 s, and corresponding rapid programmatic calculations to generatemeasurements of the model components, then programmatic calculation ofone or more DRI risk scores using one or more defined risk models.

However, in some embodiments, it is contemplated that alternate (watersuppression) pulse sequences can be used to suppress proteins and revealsmall metabolites that can be quantified.

Inflammatory Markers

The model may include one or more inflammatory markers including: GlycAand GlycB.

Other Metabolites

FIG. 3C illustrates NMR detectable metabolites that may be quantified inNMR spectra using defined deconvolution models of lipoproteins. Asshown, the DRI model may include one or more branched chain amino acidsincluding one or more of isoleucine, leucine, and valine (as discussedabove) or one or more NMR detectable metabolites such as lactatealanine, acetone, acetoacetic acid, and beta-hydroxybutiric acid withclosely aligned calculated and measured lines (for contemplatedassociated deconvolution models) for each illustrated. The associatedcenters of peak regions for the respective metabolites isoleucine,leucine, valine, lactate, and alanine are shown in FIG. 3C (0.90 ppm,0.87 ppm, 1.00 ppm, 1.24 ppm, and 1.54 ppm, respectively).

It is contemplated that other metabolites can be measured by NMR,perhaps using a CPMG pulse sequence, can include choline,phosphocholine, glycine, glycerol, a- and b-hydroxybutyrate andcarnitine.

LP-IR Score

The risk progression model may include the LP-IR™ score as a separatecomponent from another lipoprotein component.

Lipoprotein Components

The model can include one or more (typically a plurality of) lipoproteincomponents such as, for example, HDL-P, LDL-P, ratios, sums, and/orproducts of lipoprotein components. Table 2 below illustrates somecorrelations identified with exemplary lipoproteins.

TABLE 2 MESA, New Diabetes prediction Logistic regression, modelsadjusted on gender, age, Glucose 90-110 mg/dL ethnicity All, N = 411(4983) N = 210 (2038) NMR parameters X2 P X2 P VLDL size 73.5 <0.000127.1 <0.0001 LDL size −51.3 <0.0001 −18.5 <0.0001 HDL size −47.7 <0.0001−12.4 0.0004 Large VLDL 37.9 <0.0001 7.74 0.0054 Medium VLDL 2.57 0.10880.27 Small VLDL −1.72 −2.6 0.1067 IDL −0.017 −0.32 Large LDL −15.47<0.0001 −6.83 0.009 Small LDL 51.4 <0.0001 17.03 <0.0001 Large HDL −45.5<0.0001 −10.31 0.0013 Medium HDL −24.4 <0.0001 −9.5 0.0021 Small HDL43.6 <0.0001 13.1 0.0003

In some embodiments, values of some or all of the different parameterscan be derived from a single nuclear magnetic resonance (NMR) spectrumof a biosample, typically a fasting blood plasma or serum sample.

The DRI models can include lipoprotein subclass/size parameters andGlycA. In some preferred embodiments, the DRI mathematical model alsoincludes the branched-chain amino acid valine. Optionally, glucose mayalso be considered as a risk parameter in the DRI model. Where used, apatient's glucose measurement may also be obtained from the NMR spectrumof the biosample or may be obtained in other conventional manners.

The DRI model(s) can incorporate lipoprotein subclass parameters thatare known to be drug-resistant or drug-sensitive along with the insulinsensitive lipoproteins. The DRI mathematical model(s) can include genderas a variable or may be configured as different models for differentgenders. The DRI models for a respective patient can be electronicallyadjusted or selected depending on one or more factors associated withthe patient, e.g., gender of the patient, age of the patient, whetherthe patient is on a certain type of medication and the like.

FIG. 1 is a graph that presents diabetes conversion rates of subjectswithin 6 glucose subgroups (dotted line), for those subjects in theupper and lower quartile of diabetes risk index values within eachglucose stratum. As shown, the risk of developing diabetes at any givenglucose level is substantially greater for DRI index values in Q4 vs Q1.Notably, the diabetes risk index can actually be a better predictor thanglucose alone. It is contemplated that an NMR-based diabetes risk scorecan effectively stratify risk without requiring additional clinicalinformation.

The diabetes risk index calculation can be carried out to generatediabetes risk index correlated to ranges of fasting plasma glucose (FPG)mg/dL, which typically ranges from about 90-126, with some people havingless and some having higher values at each end of the range. As notedabove FPG values 100-125 mg/dL can be associated with “prediabetes.”Values between 100-110 FPG mg/dl are typically associated with increased“early” intervention risk. Values between 110-125 FPG mg/dL aretypically associated with later stage or greater risk than the “early”stage or lower FPG values.

A person's risk of developing T2DM can be presented as a DRI index scorewith respect to a defined range of risk, from low to high risk. The“index” can be a simple guide or predictor of a person's risk status.The diabetes risk index is generated from a statistically validatedmathematical model of risk that can characterize a subject's risk ofdeveloping T2DM in a future timeframe, in a range of from low (e.g.,less likely) to high (more likely) relative to a population norm. The“low” value can be associated with DRI values that are in the lower halfof a population norm. High risk DRI values can be associated with DRIvalues in a fourth quartile or fourth or fifth quintile of a populationnorm and indicates a high likelihood of convergence to type 2 diabeteswithin the next 5-7 years and therefore having prediabetes. Intermediaterisk DRI values can be associated with values in a top half of apopulation norm but below high risk values.

While it is contemplated that the DRI index will be particularly usefulwhen provided as a numerical score, the risk index can be presented on apatient report in different manners. The DRI index can be provided as aresult expressed numerically or alphanumerically, typically comprising anumerical score on a defined scale or within a defined range of values.For example, in particular embodiments, the DRI risk index can beprovided as or include a score within a defined range, such as, forexample, between 0-0.1, 0-1, 0-10, 0-24, 0-100, or 0-1000 and the like.Typically, the lowest number is associated with the least risk and thehigher numbers are associated with increased risk of developing T2DM inthe future, typically within 5-7 years although over time frames may beused for some embodiments. The lower value in the range may be above “0”such as 1, 2, 3, 4 or 5 and the like, or may even be a negative number(e.g., −1, −2, −3, 4, −5 and the like). Other index examples, include,for example, alphanumeric indexes or even icons noting degrees of risk,including but not limited to, “LR1” (low risk), IRS (intermediate risk)and “HR9” (high risk), terms such as “DRI positive”, “DRI high”, “DRIneutral”, “DRI low”, “DRI good”, “DRI bad”, “DRI watch” and the like.

As noted above, the diabetes risk index models can include or omitglucose as a parameter or may use FPG or other glucose measurement as aseparate parameter used with a DRI score to characterize risk.

In some embodiments, for example, where the a patient has a diabetesrisk index that is in the fourth quartile or fourth or fifth quintile ofa population norm, the patient can be identified as having prediabetes,as a likelihood of having prediabetes, as at risk for diabetes, ashaving modest hyperglycemia, as having impaired fasting glucose orimpaired glucose tolerance or being insulin resistant.

In some embodiments, the diabetes risk index can be provided without aglucose measurement and/or without including such a measurement in themodel. Thus a DRI score in the fourth quartile or fourth or fifthquintile, alone, can identify those at risk of developing diabetes inthe next 5-7 years.

In some embodiments, such as where the diabetes risk index is in thethird or fourth quartile or in the fourth or fifth quintile and has aglucose measurement (e.g., for FPG measurement) that is above 95 mg/dl,typically between 100-125 mg/dl, the patient can be identified as havingprediabetes or as at increased risk or associated with a likelihood ofdeveloping T2DM in the future, typically within about 5-7 years butother time frames may be utilized. Other glucose measurements may alsobe used (see, for example, Table 1).

As shown with respect to FIG. 1 , the risk of developing diabetes in thefuture at any given glucose level is substantially greater where the DRIvalue is in Q4 versus Q1. Thus, the DRI can be a simple NMR-based riskscore that can effectively stratify risk without requiring additionalclinical information.

In some embodiments, gender may be a factor in the diabetes risk indexmodel. In some embodiments, age may be a factor in the diabetes riskindex model. In other embodiments, the diabetes risk index can excludeeither gender or age considerations so as to avoid generating falsenegatives or false positives based on data corruption of such ancillarydata not directly tied to a biosample, for example.

The DRI index can be generated in one or more than one way for aparticular patient and two or more DRI indexes can be generated forcomparison, for elective use of one or both by a clinician and/orpresented as a ratio of the two. For example, the NMR data can beevaluated using a plurality of different DRI index calculation modelswhere one model can employ lipoprotein components that are sensitive toa particular drug therapy or class of therapies (e.g., statins) and onethat includes lipoprotein components that are insensitive (or immune) tosuch particular therapy or class of therapies.

TABLE 3 Lipoprotein Components/Parameters Statin Sensitivity/ResistanceAffected by statins - YES Affected by statins - NO VLDL size — NO SmallHDL-P — NO Large VLDL-P Yes Small LDL-P Yes — Large HDL-P Yes LDL sizeYes HDL size Yes Medium HDL-P Yes

By way of another example the DRI risk score can be calculated in two ormore ways, one using components that are more robust where non-fasting(blood plasma or serum) biosamples are analyzed and one that may have abetter risk prediction value but is for fasting biosamples. For example,VLDL components may be reduced or excluded from DRI models fornon-fasting samples. Table 4 lists lipoprotein components that can beaffected by nonfasting.

TABLE 4 Lipoprotein components sensitive to nonfasting. LipoproteinSensitivity to Components Nonfasting LPIR Great Large VLDL Great MediumVLDL Great Small VLDL Great VLDL size Great Small LDL-P Minimal LargeLDL-P Minimal LDL size Minimal Large HDL-P Minimal Medium HDL-P ModerateSmall HDL-P Moderate HDL size Minimal GlycA Minimal Valine MinimalGlucose Great

Similarly, the DRI risk evaluations can be calculated using both thestatin sensitive and statin insensitive DRI models allowing theclinician rather than the testing protocol analysis circuit to asseswhich applies to a particular patient. The different values can bepresented in a report or on a display with a comment on the differentresults.

In some embodiments, only one DRI score is provided to the clinicianbased on data electronically correlated to the sample or based onclinician or intake lab input, e.g., fasting “F” or non-fasting “NF,”and statin “S” or non-statin “NS” characterizations of the patient whichdata can be provided on labels associated with the biosample to beelectronically associated with the sample at the NMR analyzer 22.Alternatively, the patient characterization data can be held in acomputer database (remote or via server or other defined pathway) andcan include a patient identifier, sample type, test type, and the likeentered into an electronic correlation file by a clinician or intakelaboratory that can be accessed by or hosted by the intake laboratorythat communicates with the NMR analyzer 22. The patient characterizationdata can allow the appropriate DRI model to be used for a particularpatient.

HDL-P can be in μmol/L units. VLDL-P and LDL-P can be in units ofnmol/L. Valine and/or GlycA, where used, can be in arbitrary units orone or each can be multiplied by a respective defined conversion factorto provide the number in units of μM/μmol/L, respectively (see, e.g.,FIG. 10 for GlycA).

It is contemplated that a diabetes risk index can be used to monitorsubjects in clinical trials and/or on drug therapies, to identify drugcontradictions, and/or to monitor for changes in risk status (positiveor negative) that may be associated with a particular drug, a patient'slifestyle and the like, which may be patient-specific.

Further discussion of diabetes risk prediction models is provided belowafter discussion of examples of NMR signal deconvolution methods thatcan be used to obtain the GlycA/GycB measurement and, where used, avaline measurement.

FIG. 2 illustrates the resonant peak regions for GA associated withGlycA and GB associated with GlycB in the plasma NMR spectrum. One orboth of these peak regions can include signal that can be defined asinflammation markers in the plasma NMR spectrum.

FIGS. 5A/5B illustrates the chemical structure of the carbohydrateportion of N-acetylglycosylated proteins showing the CH₃ group thatgives rise to the GlycA NMR signal. FIGS. 6A/6B illustrates the chemicalstructure of the carbohydrate portion of N-acetylneuraminic acid (alsocalled sialic acid) modified glycoproteins showing the CH₃ group thatgives rise to the GlycB NMR signal.

FIG. 3A is an example of a deconvolved signal shown in FIG. 3B with aquartet of valine signals identified to generate a calculated Vc andmeasured Vm spectrum of the valine peaks of valine signal that can beused to measure valine (in the embodiment shown, the biosample is bloodplasma or serum. One or more of the quartet of peaks of valine arelocated within a region between about 0.72 ppm to about 1.07 ppm.Typically, one or more of the quartet of peaks of valine are locatedbetween 0.9 ppm and 1.03 ppm, e.g., at a center of the multiplet regionat about 1.00 ppm of the downfield methyl doublets. One or more of thequartet of peaks of valine can be used to measure valine in thebiosample. The concentration of valine in urine is significantly higherthan that in serum/plasma, and peak position and amplitude will bedifferent.

FIG. 3B is an enlarged region of plasma NMR spectrum (1.07-0.62 ppm)containing methyl signals from lipoproteins and branched-chain aminoacids (leucine, valine and isoleucine).

As shown in FIG. 3C, the DRI model may include one or more ofisoleucine, leucine, valine (as discussed above), lactate, and alaninewith closely aligned calculated and measured lines (for contemplatedassociated deconvolution models) for each illustrated. The associatedcenters of peak regions of the respective metabolites are shown in FIG.3C (0.90 ppm, 0.87 ppm, 1.00 ppm, 1.24 ppm, and 1.54 ppm, respectively).

FIG. 4A is an NMR spectrum of a serum sample with a glucose spectrumshown below the upper spectrum. There is a glucose multiplet at 5.2 ppm,and glucose mulitplets centered at 4.6, 3.9, 3.8, 3.7, 3.5, 3.4 and 3.2ppm which could be used for measuring glucose using NMR derivedmeasurements.

FIG. 4B shows the proton NMR spectrum of blood plasma, with the tworegions (region 1 and region 2) containing the signals produced byglucose indicated. In some particular embodiments, the peaks in region 1in the range of 3.81-4.04 ppm can be used for glucose analysis accordingto some embodiments of the present invention. Alternatively, the peaksin region 2 in the range of 3.50-3.63 ppm can be used for the glucoseanalysis of the present invention. Additionally, the combination of thepeaks in region 1 and region 2, may be used for the quantitativedetermination of glucose according to the present invention. The datapoints in the reference or standard spectrum and patient glucose samplespectra are aligned using a line-shape fitting process as describedherein to find the “best fit,” and the intensity of the standardspectrum is scaled to match the sample spectrum. The glucoseconcentration of the standard is multiplied by the scaling factor usedto match the sample lineshape to give the glucose concentration of theblood sample. See, e.g., U.S. Pat. No. 6,518,069 for further discussionof the glucose and lipoprotein measurements for assessing risk ofdeveloping Type 2 diabetes, the contents of which are herebyincorporated by reference as if recited in full herein.

Thus, in some embodiments, a patient glucose measurement can be obtainedvia NMR analysis of the biosample NMR spectrum, along with lipoproteinparticle measurements, GlycA and valine measurements. However, glucosemeasurements, where used, can alternatively be obtained in conventionalchemical ways.

It is contemplated that NMR measurements of GlycA, valine, andlipoproteins of a single (blood/plasma) in vitro biosample can provideimportant clinical information and/or further improve a prediction orevaluation of a patient or subject's risk of developing type 2 diabetesand/or having prediabetes.

FIG. 7A illustrates an enlarged chemical shift portion of the NMRspectrum between 2.080 and 1.845 ppm as shown in FIG. 2 . FIG. 7A alsoillustrates both the calculated C signal and the measured (composite)signal envelope Cm from the allylic protons of the lipids in VLDL, LDLand HDL, with underlying deconvolved GlycA and GlycB and other resonantpeaks. GlycA can include contributions from 2-NAcGlc and 2-NAcGal methylgroups. GlycB includes signal from the N-acctyl methyl groups on thesialic acid moieties of glycoproteins.

A defined lineshape GlycA mathematical deconvolution model can be usedto measure the GlycA. The “composite” or measured signal envelope Cm canbe deconvolved to quantify the signal contributions of GlycA and othercontributing components such as lipoprotein subclass components. Thedeconvolution calculates signal amplitudes of the componentscontributing to the measured signal shapes and calculates the sum of thecomponents. A close match between the calculated signal C and themeasured signal Cm indicates the deconvolution successfully modeled thecomponents that make up the NMR signal.

The peak region of the GlycA region GA and the peak of the GlycB regionGB are shown by the peaks centered at 2.00 ppm and 2.04 ppm (at about 47deg C. sample temperature), respectively, underlying the composite(upper) envelope signal line Cm. In some embodiments, the peak regionsfor GlycA and GlycB can include adjacent smaller nearby signals in thedeconvolution model to account for GlycA and GlycB signals of slightlydifferent frequency.

The protein signal Ps includes “humps” or peaks P_(GA) and P_(GB) thatalign with GA and GB, respectively. GlycA can be calculated using thedifference between total plasma GlycA signal or “GA” as given by thetotal peak area of the plasma GlycA signal and “P_(GA)”, that portion ofGlycA that may derive from the non-inflammatory glycoproteins in theprotein (d>1.21 g/L) component of plasma. The deconvolution can becarried out to subtract out the (patient/subject) variable “clinicallynon-informative” part of the total NMR signal at the GA region to leavethe more informative disease association measure of GlycA.

Stated differently, while not being bound to any particular theory, insome embodiments, the measured GlycA signal at 2.00 ppm can be referredto as GA, the deconvolution can separate it into three parts: 1) thepart contributed to by the protein (d>1.21 g/L) chosen to be largelydevoid of inflammatory proteins, 2) the part contributed to by the“non-inflammatory” lipoproteins (d<1.21 g/L), and 3) the inflammatoryglycoproteins (both lipoprotein and protein), the latter modeled by theoverlapping Lorentzians (LGA) or other curve fit functions. Theclinically informative GlycA from the deconvolution can be defined as GAminus P_(GA) and minus the non-inflammatory lipoprotein components=LGA.GlycB can be determined in a similar manner using the GB minus P_(GB)signal contribution minus the non-inflammatory lipoprotein components.

The lineshape deconvolution can be achieved with a non-negative leastsquares fitting program (Lawson, C L, Hanson R J, Solving Least SquaresProblems, Englewood Cliffs, N.J., Prentice-Hall, 1974). This avoids theuse of negative concentrations which can lead to error especially in lowsignal to noise spectra. Mathematically, a suitable lineshape analysisis described in detail for lipoproteins in the paper by Otvos, J D,Jeyarajah, E J and Bennett, D W, Clin Chem, 37, 377, 1991. A syntheticbaseline correction function may also be used to account for baselineoffsets from residual protein components. This can take the form of aquadratic or other polynomial function. Weighting factors are determinedand the fit can be optimized by minimizing the root mean squareddeviation between the experimental and calculated spectrum. See, e.g.,U.S. Pat. Nos. 4,933,844 and 6,617,167 for a description of deconvolvingcomposite NMR spectra to measure subclasses of lipoproteins, thecontents of which are hereby incorporated by reference as if recited infull herein. See also, U.S. Pat. No. 7,243,030 for a description of aprotocol to deconvolve chemical constituents with overlapping signalcontribution, the contents of which are hereby incorporated by referenceas if recited in full herein.

FIGS. 7B and 7C illustrate the composite (measured) signal “Cm” of theNMR spectra of FIG. 7A with a fitting region F_(R) corresponding to theNMR spectrum between 2.080 and 1.845 ppm. The fitting region F_(R)typically comprises 315 data points but more or less may be used, suchas between about 200-400 data points, for example. The GlycAquantification/deconvolution model includes VLDL/chylos components, LDLcomponents, and HDL components. Table 5 shows various TRLs that may beused in an exemplary DRI model.

TABLE 5 Characteristics of Triglyceride Rich L_(i)poprotein SubclassesMeasured by NMR LipoProfile ® Analysis TRL Subclass NMR ChemicalEstimated Diameter Subclass Co_(m)ponents Shift (ppm) (nm) ChylomicronsC-260 0.8477 260 Chylomicrons C-250 0.8470 250 Chylomicrons C-240 0.8464240 Chylomicrons C-225 0.8457 225 Chylomicrons C-200 0.8443 200Chylomicrons C-190 0.8440 190 Chylomicrons C-185 0.8436 185 ChylomicronsC-180 0.8429 180 Chylomicrons C-175 0.8422 175 Chylomicrons C-170 0.8416170 TRL V6 V6-140 0.8402 140 TRL V6 V6-120 0.8388 120 TRL V6 V6-1000.8374 100 TRL V5 V5-80 0.8361 80 TRL V5 V5-70 0.8347 70 TRL V5 V5-600.8333 60

The term “TRL V6” refers to TRL (triglyceride rich lipoprotein)particles or sub-fractions having a diameter between about 90 nm up toas much as about 170 nm, more typically having diameters between about100-140 inn. The term “TRL V6” can also be defined with respect to thelipid methyl group NMR signal chemical shifts (ppm) corresponding to theestimated diameters as provided in Table I below.

The term “TRL V5” refers to large TRL particles having a diameter ofbetween about 60 nm and about 80 nm (see Table 5 above for theassociated NMR chemical shifts).

The terms “chylomicron” and “chylos” refer to very large TRL particleshaving diameters that arc larger than TRL V6. As such chylomicronsreters to TRL particles or sub-fractions having a diameter between fromabout 170 nm up to about 260 nm (see Table 5 above for their associatedNMR chemical shifts). There is not a clear demarcation between TRL V5and TRL V6 nor between TRL V6 and chylomicrons, such that there is adistribution of particle sizes for each subgroup that overlaps in therange between about 80-90 nm for TRL V5-6 and between about 140-170 nmfor TRL V6 & chylomicrons.

When the TRLs are quantified, the concentrations in particleconcentration units (nmol/L) or triglyceride concentration units (mg/dL)can be expressed. Thus, for each of the different definitions of “largeVLDL”, either the particle concentrations or triglyceride concentrationscould be used in the DRI model. Without wishing to be bound to anyparticular theory, based on linear regression analysis, the triglycerideconcentration units may yield marginally better diabetes riskprediction.

FIG. 7D is a table of different components in a GlycA/B deconvolutionmodel according to embodiments of the present invention. Metabolite A isone component that can be measured in a GlycA/B deconvolution model andmay be used clinically. As illustrated in FIGS. 7E and 7F, metabolite Acan be present in a spectrum as a singlet peak and is typically presentin a sample at low concentrations (FIG. 7E), but a high concentration ofmetabolite A may be present in a sample (FIG. 7F). A plurality of curvefitting functions for the metabolite A peak region can be used toquantitatively evaluate a level of metabolite A and/or to deconvolve theNMR spectrum for quantification of GlycA and/or GlycB, for example.

The deconvolving model components shown in FIG. 7D list a plurality ofcurve fit functions Glyc1-Glyc46 that can be applied to a fitting regionthat includes the GlycA peak region and extends to a GlycB peak region(typically with between about 40-50 curve fit functions, shown as with46, but less or more such curve fit functions may be used, e.g., between30-70). As will be discussed further below, the GlycA measurement can becarried out by summing values of a defined first subset of the curve fitfunctions, values associated with all or some of the Glyc1-Glyc26components, for example. The GlycB measurement can be carried out bysumming values of a second (typically smaller) defined subset of thecurve fit functions, such as some or all components between Glyc27 andGlyc 46, for example.

FIGS. 8A-8D illustrate spectral overlaps from triglyceride richlipoproteins as the TG (triglyceride) values increase which can bechallenging to reliably deconvolve in a manner that provides precise andreliable GlycA and GlycB measurements.

The model provides sufficient HDL, LDL and VLDL/chylos components to beable to provide a good fit of the experimental signal as indicated by aclose match between calculated signal C and experimental or measuredcomposite signal Cm. Typically, the model will have more of the closelyspaced VLDL/chylos components than either LDL or HDL components as theseTRL contribute more signal to the left side of the spectrum. The modelcan include 20-50 VLDL/chylos components, typically about 25, 26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40. In a preferredembodiment, the model includes 30 VLDL/chylos components.

The model can include a plurality “N” of (typically overlapping) curvefit components N that populate a sub-region Fs of the fitting regionF_(R) that extends from a few data points (e.g., about 10 or less) tothe right of the GlycA measurement region R₁ (e.g., starting at about1.9 ppm or higher) to at least a few data points to the left of theGlycB region R₂ (and can extend to the end of the fitting region F_(R)to 2.080 ppm), at least where GlycB is measured or evaluated. Eachcomponent N, in this embodiment, can be a Lorentzian-shaped signal witha line width about 1.4 Hz. Also, in particular embodiments, each datapoint can be about 0.275 Hz apart as determined by the digitalresolution of the spectrum. The tail portion of the region Fs on theleft side may include more (Lorentzian) components than the tail portionon the right side. The number of components N in the region Fs n can beabout 46 (e.g., about 46 Lorentzians) but more or less components “N”can be used. For example, the region Fs can include, but is not limitedto, between 30-70 Lorentzians, or n=30, 35, 36. 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or60. The curves N are typically Lorentzian functions with line widths athalf-height of between 2-10 data points (0.55-2.75 Hz at 400 MHz), moretypically between 4-6 data points, and are offset from each other by adefined amount, such as, for example, 2 data points (0.55 Hz).

The GlycA and GlycB Lorentzians (or other curve fitting components N)can have the same or different numbers of data points. The GlycBLorentzians N can have the same, less or more data points than the GlycALorentzians N. The Lorentzian fit components “N” can have peak linewidths (LW) of about 1.4 Hz (at half height). However, other LWs can beused including, but not limited to, 1.1, 1.2, 1.3, 1.5 and the like. Tobe clear, data for GlycB can be omitted from the evaluation for the DRIrisk index.

GlycA can be calculated using a defined subset of the number of curvefit components N that fill the entire region R₁. GlycB can be calculatedusing a suitable number of curve fit (e.g., Lorentzian fit) components Nthat fill the entire region R₂. The region R₁ can be between 5-6 Hz. TheGlycB region R₂ can be 7-8 Hz. Optionally, the GlycA components N can beoffset by 2 data points. The GlycB components N can be offset by 4 datapoints.

GlycA can be calculated using a sum of adjacent Lorentzian components N,typically between 9-15, such as 9, 10, 11, 12, 13, 14 and 15 components.GlycB can be the sum of adjacent Lorentzian fit components N, with thesame, more, or less, typically less, than that used for GlycAmeasurements, such as between about 5-10 components N, typically about7, about 8 or about 9 components. The Lorentzians between R₁ and R₂ arenot included in the quantified measurement of either GlycA or GlycB.FIG. 7B illustrates the sum of 7 adjacent Lorcntzians used to calculatethe GlycB measurement and the sum of 10 (more narrow) Lorentzians can beused to calculate the GlycA measurements. FIG. 7C illustrates the sum of9 adjacent Lorentzians used to calculate the GlycB measurement and thesum of 12 (more closely spaced) Lorentzians can be used to calculate theGlycA measurements.

The number of HDL, LDL and VLDL components may vary for the GlycAcalculation. As shown, the HDL components can be 20 HDL components(spanning the range of HDL subclass diameters), but more or less can beused, e.g., between about 10-26. As shown, the number of LDL componentsis 9 components (representing different LDL diameters), but more or lesscan be used, e.g., between about 5-20. As shown, the number ofVLDLs/Chylos components is 30, but more or less can be used, e.g., 25-60of different size ranges.

To be clear, while a preferred embodiment describes the curve fitcomponents as Lorentzian fit components, other fitting components may beused including, but not limited to, experimental N-acetyl methyl groupsignals or Gaussian lineshape functions. Thus, any suitable curve fitfunction can be used.

FIG. 9A is a Table of different protein components (Protein 1, Protein 2and Protein 3) that, when used in the Glyc deconvolution model, yieldsdifferent GlycA concentrations and different GlycA associations with CHDevents and All-Cause Death in MESA. FIGS. 9B, 9C and 9D illustrate therespective protein signal Ps in the deconvolved spectrum and thedifferences they exhibit in the amplitudes of the signals in the GlycAand GlycB peak regions. To optimize the calculated GlycA and/or GlycBmeasurement, in some embodiments, the deconvolution model includes adefined protein signal component as discussed above. This protein signalcomponent Ps is for protein other than lipoproteins, e.g., other thanHDL, LDL, VLDL/chylos, e.g., and may be associated with the >1.21 g/Ldensity fraction of plasma obtained by ultracentrifugation (whichincludes albumin and other non-lipoprotein proteins in plasma).

This signal component “Ps” is shown in FIGS. 7A-7C. Surprisingly,although this protein signal Ps does include a peak (P_(GA), P_(GB),respectively) aligned with the peak at the chemical shift for both GlycAand GlycB, eliminating this portion of the protein NMR signal from thedeconvolution model (by, for example, digital manipulation or signalprocessing) was found to make the calculated GlycA and GlycBmeasurements relatively less clinically informative (weaker diseaseassociations). At the other extreme, including in the deconvolutionmodel a protein component with a relatively large signal at the GlycAand GlycB positions results in lower GlycA and GlycB concentrations thatare also less clinically informative, as shown for Protein #2 andProtein #3 in FIGS. 9C and 9D. Thus, by selecting an appropriate proteincomponent with an intermediate signal amplitude at the GlycA and GlycBpositions, such as Protein #1 in FIG. 9B, the deconvolution model may be“tuned” to produce GlycA and GlycB concentrations that are improvedand/or optimized with respect to their clinical associations withinflammation and related disease states.

Thus, in some embodiments, it is contemplated that the GlycA measurementwill provide a better clinical indicator if it does not include thelipoprotein signal (accounted for in the deconvolution model with theVLDL/chylo, LDL and HDL components) and if it includes only a portion ofthe remaining NMR signal, e.g., it does not include all other NMRprotein signal at the GlycA peak region. This subset of the NMR signalat the GlycA peak region may be more reflective of inflammatory proteinactivity, e.g., N-acetyl methyl signals from glycosylated acute phaseproteins.

FIG. 10 is a screen shot of the deconvolution of a 10 mmol/L referencestandard sample of N-acetylglucosamine, from which a conversion factorof 17.8 was determined to transform signal area concentrations of GlycAand GlycB to μmol/L glycoprotein N-acetyl methyl group concentrations.In some embodiments, according to MESA subjects, first to fourthquartile (mean) levels of GlycA can be: Q1: 21.6*17.8, Q2: 25.8*17.8,Q3: 29.3*17.8 and Q4: 35.3*17.8.

GlycA measurement precision using the model shown in FIG. 7B was shownto be good. A within-run (5 pools from 2009) analysis of lowestGlycA=40.5 (CV=2.47%) and highest GlycA=58.4 (CV=1.6%). Within-labresults from 13 pools from 2010 and 2011 had a lowest GlycA=25.6(CV=4.08%) and highest GlycA=69.1 (CV=1.87%). These concentrations areexpressed as “arbitrary units” of NMR signal areas and can be multipliedby 17.8 to convert them to μmol/L N-acetyl methyl group concentrations.

It is believed that the measured amplitude of the GlycA signal in anyone sample may have the advantage of providing a more stable and“time-integrated” measure of the patient's inflammation state than isprovided by measurements of hs-CRP or other individual inflammatoryproteins.

As noted above, FIG. 10 illustrates a conversion factor that can be usedto calculate measurements of GlycA. The GlycA measurement can also be aunit less parameter as assessed by NMR by calculating an area under apeak region at a defined peak in NMR spectra. In any event, measures ofGlycA with respect to a known population (such as MESA) can be used todefine the level or risk for certain subgroups, e.g., those havingvalues within the upper half of a defined range, including values in thethird and fourth quartiles, or the upper 3-5 quintiles and the like.

FIGS. 12A-12C are exemplary flow diagrams of operations that can be usedto obtain NMR signal associated with valine according to embodiments ofthe present invention.

FIG. 12A illustrates that a pre-analytical evaluation (block 710) canoccur before a valine region of the NMR signal is determined (block725), then deconvolved (block 750). FIG. 12B illustrates an exemplarypre-analytical evaluation 810 which includes delivery verification ofthe sample into the flow cell as either complete failure (block 812) orpartial injection failure (block 813), shimming verification (block815), temperature verification (block 817) and a citrate tube detection(failure) (block 819), all using defined characteristics of signalassociated with a defined diluent added to the sample.

Referring again to FIG. 12A, once the defined parameters are confirmedwithin limits, the pre-analytical quality control analysis can end(block 720) and the determination of the valine region can be identified(block 725) and the spectrum deconvolved and valine level calculated(block 750). Optionally, a post-analytical quality control can beelectronically performed (block 755) and the results output (block 760).The results can be included in a test report with comments, visualindicia of high or low and the like (block 765).

Referring to FIG. 12C, NMR signal can be electronically obtained of anin vitro biosample with a defined added diluent (block 902). The QCevaluation can be carried out (block 910). The valine region isdetermined (block 925). The valine region is deconvolved (block 950 d)and an NMR derived value of valine is calculated (950 c).

The diluents can comprise calcium ethylenediamine tetraacetic acid (CaEDta) (block 903) or other suitable diluent that creates a reliable peakand behaves in a predictable manner. Well established chemical shift orquantitation references include, for example, formate,trimethylsilylpropionate (and isotopically labeled isomers), and EDTA.

The pre-analytical quality control evaluation 810 can be based oninspection of characteristics of the CaEDTA reference peak and thesystem or processor can be configured not to perform the Valine testunless the NMR spectra have been acquired under specified conditionssuch as those shown in FIG. 12B. The sample temperature can be 47±0.5°C. in the flow cell for NMR scans/signal acquisition. The sample cancomprise diluents in a 1:1 ratio (block 905) or other defined ratio(e.g., more sample, less diluents or more diluent; less sample, e.g.,2:1 or more sample, less diluents, e.g. 1:2).

The test sample can be rejected with a defined error code if CaEDTAheight >140 for any acquired spectrum (block 919). This high value isindicative of detection of the citrate peak in conjunction with theCaEDTA peak. The citrate peak is introduced by collection of thespecimen in an improper citrate tube. By disrupting the ability tolocate the exact position of the CaEDTA peak, the citrate peak candisrupt the process for determining the Valine region.

The Valine region is located upfield relative to the position of theCaEDTA peak. The broad peaks beneath Valine are various methyl (—CH₃—)protons of lipoproteins. The CaEDTA location can be determined atapproximately 22258±398 data points (block 921). The Valine region canbe determined independently for each acquired spectrum. The Valinesignal can be modeled with suitable data points using, for example, 25data points (center ±12 data points) for each peak of the quartet or 300data points for the valine region of both doublets, but other numbers ofdata points may be used. The measurement of Valine can be carried outusing one, two, three or all four peaks of the valine peak quartet.

All basis set spectra can be linearly interpolated before utilized bythe non-negative least squares algorithm. The spectra to be analyzed andthe basis set spectra can have a zero baseline offset modificationbefore utilized by the non-negative least squares algorithm.

The start of the Valine region can be at about 2196-4355 data points,typically the latter when including both doublets, (the “Valine regionoffset”) upfield from the location of the CaEDTA peak (block 922). Insome embodiments, the start of the valine region is at 4355 data pointsupfield from the location of the CaEDTA peak.

In some embodiments, the valine quantification is carried out bycharacterizing the valine resonances at between 0.0-1.01 ppm as twodoublets. Three or more valine experimental spectra stepped by two datapoints can be used as basis sets to model valine signal. The centervaline peaks can be located by sliding three valine components +/−15data points and determined through a least squares sum minimization. Thevaline signal can be modeled with a total of about 300 data points.

Each basis set, including those used for the baseline but excluding theDC offset, are offset such that the lowest value is subtracted from thefunction (making the lowest point equal to 0). This prevents inclusionof a DC offset in the shapes they represent.

The Valine region from each acquired spectrum is deconvolved with aseries of analyte and baseline functions which have been treated to thesame type of pre-processing as the acquired spectra. The deconvolutioncoefficient for each component can be multiplied by an associatedconversion factor. The current Valine embodiment has a conversion factorof 2271 to report Valine in μM units; however, this value can vary by±10% without unduly affecting the reported value significantly.

Basis Function Starting Component Conversion position relative to NameFilename Factor CaEDTA Valine1 Valine318LB019.1r 2271 −4353 Valine2Valine318LB019.1r 2271 −4355 Valine3 Valine318LB019.1r 2271 −4357

The resulting values are summed. Result values produced independentlyfor each acquired spectrum can be averaged to generate final values touse in the measurement.

Data can be acquired using presaturation water suppression from a 1:1diluted sample and can include between 5-20 scans, typically about 10scans stored as 5 blocks of 2 (5 FIDs consisting of 2 scans each) (block926).

The pulse sequence used in conjunction with presaturation watersuppression can optionally include a presaturation (water suppression)pulse and a suitable excitation pulse. FIDs can be acquired with 9024data points with a sweep width of 4496.4 Hz. Each FID can be multipliedwith a shifted Gaussian function:

$e^{- {(\frac{({t - {gfs}})}{gf})}^{2}},$or in computer terms, exp(−((t−gfs)/gf){circumflex over ( )}2), wheregfs=0.2 seconds and gf=0.2 seconds.

This can be performed prior to Fourier transformation with zero-fillingwhich yields the frequency-domain GM spectrum for each FID consisting of16,384 data points (block 927). The spectra can be phased using thecalibration-specified phase value. The spectra can be scaled(multiplied) by a calibration-specified scaling factor. All basis setspectra can be linearly interpolated before utilized by the non-negativeleast squares algorithm. The spectra to be analyzed and the basis setspectra can have a zero baseline offset modification before utilized bythe non-negative least squares algorithm (e.g., all components used forthe model and the spectrum that will be analyzed can be linearlyinterpolated) (block 928). To determine the center of the valine fittingregion, the valine resonances between 0.9 and 1.0 as two doublets can becharacterized and the center peaks can be identified by sliding threevaline components ±15 data points (block 929).

FIG. 13 is a chart of prospective associations of hs-CRP and NMRmeasured GlycA and Valine levels with various exemplary disease outcomesbased on MESA data (n≈5680). The chart was generated from logisticregression analyses adjusted for age, gender, race, smoking, systolicblood pressure, hypertension medications, body mass index, diabetes,LDL-P and HDL-P. The likelihood ratio statistic χ² gives a quantitativemeasure of the extent to which the indicated variable improves diseaseprediction when added to the 10 covariates in the regression model. Theanalyses used GlycA measurement values from the deconvolution modelshown in FIG. 7B. The right side column shows that GlycA and Valine areadditive in their associations with disease when they both havesignificant associations examined separately.

FIG. 14 is a Table of Characteristics of MESA subjects by NMR measuredGlycA quartile. The mean GlycA level of those in the 3rd quartile is29.3. This table shows that people with higher GlycA levels havecharacteristics associated with higher inflammation (more smoking,hypertension, hs-CRP, etc). NMR signal area units can be called“arbitrary” units. The GlycA levels in this table are in these“arbitrary units” that may be converted to methyl group concentrationunits (umol/L) by multiplying by 17.8.

Referring now to FIG. 15 , it is contemplated that most, if not all, themeasurements can be carried out on or using a system 10 in communicationwith or at least partially onboard an NMR clinical analyzer 22 asdescribed, for example, with respect to FIG. 16 below and/or in U.S.Pat. No. 8,013,602, the contents of which are hereby incorporated byreference as if recited in full herein.

The system 10 can include a Diabetes Risk Index Module 370 to collectdata suitable for determining the DM (e.g., GlycA, valine). The system10 can include an analysis circuit 20 that includes at least oneprocessor 20 p that can be onboard the analyzer 22 or at least partiallyremote from the analyzer 22. If the latter, the Module 370 and/orcircuit 20 can reside totally or partially on a server 150. The server150 can be provided using cloud computing which includes the provisionof computational resources on demand via a computer network. Theresources can be embodied as various infrastructure services (e.g.computer, storage, etc.) as well as applications, databases, fileservices, email, etc. In the traditional model of computing, both dataand software are typically fully contained on the user's computer; incloud computing, the user's computer may contain little software or data(perhaps an operating system and/or web browser), and may serve aslittle more than a display terminal for processes occurring on a networkof external computers. A cloud computing service (or an aggregation ofmultiple cloud resources) may be generally referred to as the “Cloud”.Cloud storage may include a model of networked computer data storagewhere data is stored on multiple virtual servers, rather than beinghosted on one or more dedicated servers. Data transfer can be encryptedand can be done via the Internet using any appropriate firewalls tocomply with industry or regulatory standards such as HIPAA. The term“HIPAA” refers to the United States laws defined by the Health InsurancePortability and Accountability Act. The patient data can include anaccession number or identifier, gender, age and test data.

The results of the analysis can be transmitted via a computer network,such as the Internet, via email or the like to a patient, clinician site50, to a health insurance agency 52 or a pharmacy 51. The results can besent directly from the analysis site or may be sent indirectly. Theresults may be printed out and sent via conventional mail. Thisinformation can also be transmitted to pharmacies and/or medicalinsurance companies, or even patients that monitor for prescriptions ordrug use that may result in an increase risk of an adverse event or toplace a medical alert to prevent prescription of a contradictedpharmaceutical agent. The results can be sent to a patient via email toa “home” computer or to a pervasive computing device such as a smartphone or notepad and the like. The results can be as an email attachmentof the overall report or as a text message alert, for example.

Referring now to FIG. 16 , a system 207 for acquiring at least one NMRspectrum for a respective biosample is illustrated. The system 207includes an NMR spectrometer 22 s and/or analyzer 22 for obtaining NMRdata for NMR measurements of a sample. In one embodiment, thespectrometer 22 s is configured so that the NMR signal acquisition isconducted at about 400 MHz for proton signals; in other embodiments themeasurements may be carried out at between about 200 MHz to about 900MHz or other suitable frequency. Other frequencies corresponding to adesired operational magnetic field strength may also be employed.Typically, a proton flow probe is installed, as is a temperaturecontroller to maintain the sample temperature at 47+/−0.5 degrees C. Thespectrometer 22 is controlled by a digital computer 214 or other signalprocessing unit. The computer 211 should be capable of performing rapidFourier transformations. It may also include a data link 212 to anotherprocessor or computer 213, and a direct-memory-access channel 214 whichcan connects to a hard memory storage unit 215 and/or remote server 150(FIG. 15 ).

The digital computer 211 may also include a set of analog-to-digitalconverters, digital-to-analog converters and slow device I/O ports whichconnect through a pulse control and interface circuit 216 to theoperating elements of the spectrometer. These elements include an RFtransmitter 217 which produces an RF excitation pulse of the duration,frequency and magnitude directed by the digital computer 211, and an RFpower amplifier 218 which amplifies the pulse and couples it to the RFtransmit coil 219 that surrounds sample cell 220 and/or flow probe 220p.The NMR signal produced by the excited sample in the presence of a 9.4Tesla polarizing magnetic field produced by superconducting magnet 221is received by a coil 222 and applied to an RF receiver 223. Theamplified and filtered NMR signal is demodulated at 224 and theresulting quadrature signals are applied to the interface circuit 216where they are digitized and input through the digital computer 211. TheDRI risk evaluation Module 370 or analysis circuit 20 (FIG. 15, 17 ) ormodule 350 (FIG. 16 ) or can be located in one or more processorsassociated with the digital computer 211 and/or in a secondary computer213 or other computers that may be on-site or remote, accessible via aworldwide network such as the Internet 227.

After the NMR data are acquired from the sample in the measurement cell220, processing by the computer 211 produces another file that can, asdesired, be stored in the storage 215. This second file is a digitalrepresentation of the chemical shift spectrum and it is subsequentlyread out to the computer 213 for storage in its storage 225 or adatabase associated with one or more servers. Under the direction of aprogram stored in its memory, the computer 213, which may be a personal,laptop, desktop, workstation, notepad, tablet or other computer,processes the chemical shift spectrum in accordance with the teachingsof the present invention to generate a report which may be output to aprinter 226 or electronically stored and relayed to a desired emailaddress or URL. Those skilled in this art will recognize that otheroutput devices, such as a computer display screen, notepad, smart phoneand the like, may also be employed for the display of results.

It should be apparent to those skilled in the art that the functionsperformed by the computer 213 and its separate storage 225 may also beincorporated into the functions performed by the spectrometer's digitalcomputer 211. In such case, the printer 226 may be connected directly tothe digital computer 211. Other interfaces and output devices may alsobe employed, as are well-known to those skilled in this art.

Embodiments of the present invention may take the form of an entirelysoftware embodiment or an embodiment combining software and hardwareaspects, all generally referred to herein as a “circuit” or “module.”

As will be appreciated by one of skill in the art, the present inventionmay be embodied as an apparatus, a method, data or signal processingsystem, or computer program product. Accordingly, the present inventionmay take the form of an entirely software embodiment, or an embodimentcombining software and hardware aspects. Furthermore, certainembodiments of the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputer-usable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

The computer-usable or computer-readable medium may be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, device, or propagation medium. Morespecific examples (a non-exhaustive list) of the computer-readablemedium would include the following: an electrical connection having oneor more wires, a portable computer diskette, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, and a portable compactdisc read-only memory (CD-ROM). Note that the computer-usable orcomputer-readable medium could even be paper or another suitable medium,upon which the program is printed, as the program can be electronicallycaptured, via, for instance, optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java7, Smalltalk, Python, Labview, C++, or VisualBasic. However, thecomputer program code for carrying out operations of the presentinvention may also be written in conventional procedural programminglanguages, such as the “C” programming language or even assemblylanguage. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN) or secured area network (SAN), or the connectionmay be made to an external computer (for example, through the Internetusing an Internet Service Provider).

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

FIG. 17 is a block diagram of exemplary embodiments of data processingsystems 305 that illustrates systems, methods, and computer programproducts in accordance with embodiments of the present invention. Theprocessor 310 communicates with the memory 314 via an address/data bus348. The processor 310 can be any commercially available or custommicroprocessor. The memory 314 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 305. Thememory 314 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 17 , the memory 314 may include several categories ofsoftware and data used in the data processing system 305: the operatingsystem 352; the application programs 354; the input/output (I/O) devicedrivers 358; a DRI module 370 and the data 356. The Diabetes Risk IndexEvaluation Module 370 can consider the level of the measured GlycA,lipoprotein components and optionally also valine and also optionally,glucose, in a multi-parameter mathematical model of risk of developingtype 2 diabetes in the next 5-7 years or a likelihood of havingprediabetes.

The data 356 may include signal (constituent and/or composite spectrumlineshape) data 362 which may be obtained from a data or signalacquisition system 320. As will be appreciated by those of skill in theart, the operating system 352 may be any operating system suitable foruse with a data processing system, such as OS/2, AIX or OS/390 fromInternational Business Machines Corporation, Armonk, N.Y., WindowsCE,WindowsNT, Windows95, Windows98, Windows2000 or WindowsXP from MicrosoftCorporation, Redmond, Wash., PalmOS from Palm, Inc., MacOS from AppleComputer, UNIX, FreeBSD, or Linux, proprietary operating systems ordedicated operating systems, for example, for embedded data processingsystems.

The I/O device drivers 358 typically include software routines accessedthrough the operating system 352 by the application programs 354 tocommunicate with devices such as I/O data port(s), data storage 356 andcertain memory 314 components and/or the NMR spectrometer or analyzer22. The application programs 354 are illustrative of the programs thatimplement the various features of the data processing system 305 and caninclude at least one application, which supports operations according toembodiments of the present invention. Finally, the data 356 representsthe static and dynamic data used by the application programs 354, theoperating system 352, the I/O device drivers 358, and other softwareprograms that may reside in the memory 314.

While the present invention is illustrated, for example, with referenceto the Modules 350, 370 being an application program in FIG. 17 , aswill be appreciated by those of skill in the art, other configurationsmay also be utilized while still benefiting from the teachings of thepresent invention. For example, the GlycA Module 350 and/or the DRIModule 370 may also be incorporated into the operating system 352, theI/O device drivers 358 or other such logical division of the dataprocessing system 305. Thus, the present invention should not beconstrued as limited to the configuration of FIG. 17 , which is intendedto encompass any configuration capable of carrying out the operationsdescribed herein.

FIG. 18 is a flow chart of exemplary operations that can carry outembodiments of the present invention. As shown, at least one definedmathematical model or risk of progression to type 2 diabetes in a futuretime frame (e.g., 5-7 years) can be provided (block 400). Measurementsof components of the at least one mathematical model of a respectivepatient biosample can be obtained (block 403). A diabetes risk indexscore can be calculated using the model and the measurements (block405). A patient risk report (paper and/or electronic) with the DRI scorecan be provided to desired recipients (e.g., a patient and/or clinician)(block 412). The DRI score can stratify risk for different patientshaving the same glucose measurement (block 404).

The measurements can include NMR measurements of lipoprotein subclassesand either or both GlycA and valine (block 405). The measurements caninclude one or more of VLDL size, ratio of medium HDL-P to total HDL-Pand/or sum of VLDL-P (block 408).

A graph of risk of conversion versus glucose level and comparative Q1and/or Q4/Q5 references can be provided as a relative risk summary(block 413).

A graph of risk of conversion versus glucose level and comparativepatient risk with different diabetes risk score measurement and/ordifferent glucose measurement can be provided (block 414).

The risk summary can provide a relative comparison to defined populationnorms.

FIG. 19 is a flow chart of exemplary operations that can carry outembodiments of the present invention. A (measured) composite envelopeNMR spectrum of NMR spectra of a fitting region of a biosample (e.g.,blood plasma or serum) can be obtained (block 500). The NMR compositesignal envelope is electronically deconvolved using a defined modelhaving HDL, LDL and VLDL/Chylos components and a plurality of curve fit(e.g., Lorentzian) functions associated with at least a GlycA peakregion centered at a defined chemical shift location (e.g., 2.00 ppm)associated with GlycA (block 502). A defined number of curve fittingfunctions for the peak region associated with GlycA can be summed (block515). A conversion factor can be applied to the summed functions togenerate a calculated measurement of GlycA (block 520).

The method can include providing at least one defined mathematical modelof risk of progression to type 2 diabetes within 5-7 years that is usedto calculate a diabetes risk score (block 523). The method can includeidentifying whether the patient is at risk of developing type 2 diabetesand/or has prediabetes based on the defined mathematical risk model thatincludes a plurality of lipoprotein components and GlycA or valine togenerate the DRI score (block 524).

Optionally, the DRI and/or GlycA calculations can be provided in apatient and/or clinical trial report (block 522).

The defined GlycA deconvolution model can include a protein signalcomponent at a density greater than about 1.21 g/L that can bedeconvolved/separated from the signal composite envelope (block 503).

FIG. 20A is a schematic illustration of an exemplary patient test report100 that can include various lipoprotein parameters such as two or moreof DRI, GlycA, Valine, HDL-P, LDL-P 101. The DRI and/or GlycA number 101can be presented with risk assessment summary 101 s correlated topopulation norms, graphs, typical ranges, and/or degree of risk (e.g.,high, increased or low risk), shown as a sliding scale graph in FIG.20A.

However, other risk summary configurations may be used including ranges,high to low or low to high, or just noting whether the associated riskis low, medium or increased and/or high.

FIG. 20B is another example of a patient report with a visual (typicallycolor-coded) graphic summary of a continuum of risk from low to highaccording to embodiments of the present invention.

FIG. 21 illustrates that a graph 130 of DRI values over time can beprovided to illustrate a change in patient health and/or inflammatorystatus over time due to age, medical intervention or a therapy accordingto some embodiments. Tracking this parameter may provide a clinicalindicator of efficacy of a therapy and/or a better risk predictor fortype 2 diabetes for patients. As shown in FIG. 21 , the graph analysiscan be used to monitor a patient over time to correlate known start oruse of a drug or other therapy. Future drugs or uses of known drugs canbe identified, screened or tested in patients identified using DRIand/or GlycA evaluations of drugs or therapies of any suitable diseasestate including, for example, anti-diabetes and anti-obesity therapies.

The tracking can be provided via a tracking module that can be providedas an APPLICATION (“APP”) for a smartphone or other electronic formatfor ease in tracking and/or for facilitating patient compliance with atherapy.

FIGS. 22A and 22B are graphical patient/clinical reports of % risk ofdiabetes versus FPG level and DRI score and risk pathway. FIG. 22A showspatient #1's score while FIG. 22B shows patient #1's score in comparisonwith a lesser risk patient (patient number 2) having the same FPG. Whileeach patient has the same FPG, they have different metabolic issuesidentified by the DRI scores stratifying risk according to embodimentsof the present invention.

FIGS. 23A-23C are graphical patient/clinical reports of diabetesconversion rate (%) versus FPG level and DRI score (high DRI, Q4 and lowDRI, Q1) according to embodiments of the present invention. FIG. 23A isfor a 4-year risk of conversion to diabetes. FIG. 23B is a 5-year riskof conversion and FIG. 23C is a 6 year risk of conversion.

FIG. 24 is a graphical patient/clinical report of a Q4/Q1 relative riskof diabetes conversion (1-8) versus FPG level and DRI score for both6-year (upper line) and 2-year conversions periods according toembodiments of the present invention.

FIG. 25 is a graphical patient/clinical report of log scale 5 yearconversion with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

FIG. 26 is a graphical patient/clinical report of 5 year conversion todiabetes with a diabetes conversion rate (%) versus FPG level and DRIscore (high DRI, Q4 and low DRI, Q1) color coded from green, yellow,pink/orange to red and with a legend textually correlating the risk asvery high, high, moderate and low, according to embodiments of thepresent invention.

Embodiments of the invention will now be described by way of thefollowing non-limiting Examples.

EXAMPLES Example 1

A Diabetes Risk index (DRI) was developed using MESA that uses onlyinformation derived from a single nuclear magnetic resonance (NMR)spectrum of a fasting plasma sample. The DRI can identify thehighest-risk patients who are likely to benefit the most fromintervention. This information includes glucose and lipoproteinsubclass/size parameters previously linked to insulin resistance, aswell as valine, and GlycA. FIG. 1 was generated using NMR spectracollected at baseline from the Multi-Ethnic Study of Atherosclerosis(MESA). The MESA dataset consisted of 3185 participants, 280 of whomdeveloped diabetes during 5 years of follow-up. FIG. 1 presents thediabetes conversion rates of subjects within 6 glucose subgroups (dottedline), and those for subjects in the upper and lower quartile of DRIwithin each glucose stratum. As shown, the risk of developing diabetesat any given glucose level is substantially greater for DRI in Q4 vs Q1.The NMR-based diabetes risk score can effectively stratify risk withoutthe need for additional clinical information.

In this analysis, predictive modeling techniques were used to identify a“best” logistic regression model of five year diabetes conversion. Themodeling used clinical data from the MESA study as well as NMR derivedlipid and metabolite data. Although the final model selection restrictedthe data to subjects with baseline glucose less than or equal to 115,initial modeling considered the possibility of different “best” modelsfor subjects with baseline glucose less than or equal to 100, subjectswith baseline glucose greater than 100 but less than or equal to 115,and subjects with baseline glucose greater than 115 but less than orequal to 125.

One selected model for predicted five year conversion to diabetesincluded baseline glucose (glucos1c), VLDL size (vsz3), the ratio ofmedium HDL-P to total HDL-P (HMP_HDLP), medium HDL-P (hmp3), the sum oflarge VLDL-P and medium VLDL-P (vlmp3), GlycA, and valine. This modelincluded two interactions: HMP_HDLP by GlycA and vsz3 by vlmp3. It wasbuilt using data from subjects with baseline glucose less than or equalto 115.

During development of these parameters, large VLDL-P (vlp3) was replacedby vlmp3 (sum of vmp3 and vlp3) in the NMR model as it was found to givebetter predictive results. Further exploration showed that the NMR modelcould include an interaction between VLDL size and vlmp3.

The final series of analyses showed that VLDL size and vlp3 (prior tocalculation of vlmp3) could be appropriately truncated at low and highvalues without degrading the predictive accuracy. The benefit of suchtruncation may be model robustness. Also, the analysis showed thataccounting for possible non-linear effects of VLDL size and vlmp3 onfive year conversion was unnecessary.

Example 2

The DRI index model can employ lipoproteins, valine and GlycA as sevendifferent parameters (including 5 lipoprotein parameters): VLDL size,large+medium VLDL particle number, total HDL and medium HDL subclassparticle number, valine, and GlycA.

Another study of the MESA dataset consisted of 4985 non-diabeticparticipants, 411 of whom developed diabetes during 6 years offollow-up. The MESA data restricted to the 1832 individuals withintermediate glucose 90-110 mg/dL, 198 of whom converted to diabetes.Conversion rates by quintile of a baseline DRI index and the fourcomponent parts of the DRI model: lipoproteins, GlycA, valine, andglucose were assessed. Relative rates for those in the extreme quintileswere 2.2 for lipoproteins, 1.9 for GlycA, 1.7 for valine, 6.3 forglucose, and 10.7 for DRI (2.2% in Q1; 23.0% in Q5).

The results indicate that the DRI score, without any additional clinicalinformation, can identify among patients with intermediate glucoselevels those with diabetes risk differing >10-fold. Ratios between thefirst quintile and the fifth quintile establish that there is a 10.7ratio for DRI which indicates that patients can have a 10 folddifference in diabetes risk when the FPG is in the range of 90-110mg/dL.

It is believed that the new DRI scores can allow at-risk patients to betargeted for intervention before the onset of substantial beta-celldysfunction.

Example 3 MESA and IRAS Comparison

FIG. 27 is a table of data that shows the performance of DRI (withglucose) in the IRAS dataset, the MESA dataset, and IRAS dataset (usingthe glucose subgroups from MESA). When IRAS samples were collected, thedefinitions of pre-diabetes and diabetes were different than that ofMESA, which occurred years later.

FIG. 28 shows the performance of DRI (without glucose) in the samedataset criteria as FIG. 27 . The highlighted values show the differencebetween the 5th quintile and the first quintile.

IRAS: Observational study of middle-aged Hispanic, non-Hispanic white,and African-American men and women. Blood samples obtained 1992-1994.NMR analyses of thawed-refrozen heparin plasma performed in 2001 onVarian instruments (preceding Profilers or current generation NMRanalyzers used by LipoScience, Inc., Raleigh, N.C.) using WET watersuppression. NMR dataset population was 46% normoglycemic (olddefinition, glucose <110 mg/dL), 22% impaired glucose tolerance, 32%diabetic. These analyses are for the n=982 nondiabetic subjects, 134 ofwhom developed diabetes during a mean 5.2 years of follow-up.

Spearman Correlation in MESA and IRAS Logistic Regression Results:Conversion to New Diabetes in IRAS and MESA

IRAS, 134 New Diab MESA, 411 Newdiab (N = 961) (N = 4985) adjusted onglucose age gender race model param model param parameters X² X² Param PX² X² Param P LPIR 120.8 27.4 <0.0001 776.8 27.5 <0.0001 DRInmr (no108.0 16.7 <0.0001 796.2 47.9 <0.0001 glucose) DRI (LPIR) no 111.6 19.4<0.0001 796.1 46.8 <0.0001 glucose Vsz 96.8 7.9 0.005 750.1 18.8 <0.0001Lsz 101.4 9.9 0.0017 760.0 11.5 0.0007 Hsz 112.8 18.5 <0.0001 760.2 11.10.0009 Vlp 106.3 16.4 <0.0001 752.1 3.7 0.0559 Lsp 107.5 17.1 <0.0001757.1 8.7 0.0032 hlp 108.3 15.1 0.0001 756.0 7.0 0.0082 GlycA 93.2 2.3755.8 7.5 0.0063 Valine 93.7 2.7 0.099 767.5 19.1 <0.0001 lsp lsz hszhlp 120.6 HSZ 767.0 VLP vlp vsz VSZ

Example 4

The model parameters below were derived from regression models thatincluded glucose/size/metabolite/ratio/SizePlus family. The initial formof these models included baseline glucose, VLDL size (vsz3), GlycA,medium HDL-P ratio (HMP_HDLP), medium HDL-P (hmp3), large VLDL-P (vlp3),and the interaction between GlycA and medium HDL-P ratio. Additionalvariable investigation determined that gender and valine could be addedto this model, but the addition of alanine did not improve predictiveaccuracy. Further study and discussion determined that large VLDL-P(vlp3) should be replaced by the sum of large and medium VLDL-P (vlmp3).Also, exploratory analyses determined that an interaction between VLDLsize (vsz3) and vlmp3 was statistically significant in the model. Themodeling used subjects in the training dataset with baseline glucoseless than or equal to 115 for model training, and subjects in the testdataset with baseline glucose less than or equal to 115 for modeltesting.

DRI models can be based on a likelihood and/or predicted five yearconversion to diabetes. The risk evaluation models can include VLDL size(vsz3), a ratio of medium HDL-P to total HDL-P (HMP_HDLP), medium HDL-P(hmp3), a sum of large VLDL-P and medium VLDL-P (vlmp3), GlycA, andvaline.

This model includes two interactions: HMP_HDLP by GlycA and vsz3 byvlmp3. The model can optionally also include a baseline glucose(glucos1c).

Additional modeling determined that VLDL size (vsz3) and large VLDL-P(vlp3, prior to calculation of vlmp3) could be truncated withoutdegradation to predictive accuracy. For vsz3, any value less than 39.2was truncated to 39.2, and any value greater than 65.1 was truncated to65.1. For vlp3, any value less than 0.7 was truncated to 0.7, and anyvalue greater than 7.9 was truncated to 7.9.

DRIp Non LPIR Model C-statistic 0.840 Wald Standard Chi- Pr > ParameterDF Estimate Error Square ChiSq Intercept 1 −17.6413 1.6351 116.4032<.0001 Glucose 1 0.1236 0.00865 204.2648 <.0001 vsz 1 0.0928 0.020420.7076 <.0001 GlycA 1 −0.0409 0.0361 1.2822 0.2575 HMP_HDLP 1 −7.90572.7708 8.1410 0.0043 GlycA*HMP_HDLP 1 0.3066 0.0884 12.0228 0.0005 Hmp 1−0.0790 0.0325 5.9206 0.0150 VLMP 1 0.0608 0.0268 5.1522 0.0232 vsz*VLMP1 −0.00140 0.000535 6.8493 0.0089 Valine 1 0.00856 0.00310 7.6069 0.0058

DRI-LPIR Model C-statistic 0.839 Standard Wald Parameter DF EstimateError Chi-Square Pr > ChiSq Intercept 1 −14.8629 1.2903 132.6852 <.0001Valine 1 0.00756 0.00312 5.8947 0.0152 Glucose 1 0.1235 0.00867 203.0915<.0001 LPIR 1 0.0253 0.00626 16.2565 <.0001 hmp 1 −0.2651 0.0651 16.5696<.0001 vmp 1 0.0165 0.0110 2.2379 0.1347 LPIR*vmp 1 −0.00051 0.0001946.7620 0.0093 GlycA 1 −0.0170 0.0304 0.3123 0.5762 hmp*GlycA 1 0.007380.00209 12.4268 0.0004

Example 5

The Diabetes Risk Index (DRI) test can be a lab-based multivariate assaythat employs a defined mathematical model to yield a single compositescore of one's risk of developing type II diabetes in 5 years.Predictive biomarkers included in the assay include: fasting glucose,lipoprotein sub-fractions, branched chain amino acid(s) and one or moreinflammatory biomarker(s). Clinical performance is believed to besuperior to fasting glucose alone in individuals with FPG<125, and iscontinually more predictive of diabetes risk vs. FPG with loweringlevels of FPG. This is because the DRI risk score captures one'sunderlying metabolic defects by including these other predictiveanalytes, which glucose alone does not capture.

Data used to develop and validate DRI scores can be based onretrospective analysis of data from a multi-center and multi-ethnicprospective observational study with nearly 5,000 non-diabetic subjectsat baseline. The patient report for this assay can show a fastingglucose value and a single 5-year diabetes risk prediction rate (whichincludes the risk predictive value of fasting glucose along with theother biomarkers).

Example 6

The Diabetes Risk Index (DRI) model can be calculated using a pluralityof components including at least one lipoprotein component, valineand/or another branched chain amino acid, and GlycA and/or anotherinflammatory marker. The model can be adjusted to use differentcomponents based on whether the patient is on a statin or other drugtherapy known to impact DRI risk scores and/or based on whether thebiosample is a fasting or non-fasting biosample.

The DRI risk score can be calculated in a plurality of different mannersand filtered before sent to a clinician (or sent to the clinician forcorrect reporting) and patient based on defined patient criteria so thatthe patient is provided the appropriate score for the test andpatient-specific parameters for that biosample.

Example 7

The DRI model is configured to stratify risk in subjects having the sameA1C, oral glucose tolerance or FPG measurement and a different diabetesrisk score. The diabetes risk index score can be a numerical scorewithin a defined score range, with scores associated with a fourthquartile (4Q) or fifth quintile (5Q) of a population norm reflecting anincreased or high risk of developing type 2 diabetes within 5-7 years.Respective subjects that are at increased risk of developing type 2diabetes prior to onset of type-2 diabetes when glucose is in a rangethat is below prediabetes to the high end of prediabetes, e.g., afasting blood glucose (FPG) levels are between 90-125 mg/dL can beidentified. The DRI score provides information to stratify risk insubjects having the same glucose measurement and a different diabetesrisk score based on underlying metabolic issues in different patients.

Example 8

Table 6 lists potential alternative VLDL parameters that may be used ina DRI model based on a logistic regression analysis on VLDL. One or moreof the alternative VLDL parameters may optionally be used with one ormore of the components described above with respect to any of the otherDRI models and/or components thereof described in the Examples and/orDetailed Description part of the specification. Large VLDL may bereferred to as “VLP (V5p+V6p+CHYp)”, which are TRL particles rangingfrom 60-260 nm diameter. Different definitions of “large VLDL” could beused in a DRI model. For example, the chylomicrons could be excluded andmay be called TRL60-140 based on Table 5. Alternatively or in addition,TRL60-120 (without V6-140) may be used in a DRI model.

TABLE 6 Potential alternative VLDL parameters that may be used in a DRImodel with other components. Logistic regression on New Diabetes MESA N= 210/2038, model adjusted on MESA N = 210/2038, glucose (glucose modelunadjusted, 90-110 mg/dL) glucose 90-110 mg/dL X2 X2 X2 parameter X2parameter parameter (model parameter (model (model adjusted (modeladjusted on not- on not- NMR parameters glucose) adjusted) glucose)adjusted) ChyloL (185-260) −0.80 −0.77 ChyloS (170-180) −5.69 0.017−3.90 0.0482 V6tg(2) 100 + 120 5.60 0.0178 4.47 0.0344 V6tg(3) 100 +120 + 3.73 0.0534 3.13 0.0766 140 V5tg 8.31 0.0039 4.96 0.0259 VLP(V5p + V6p + 8.55 0.0035 5.13 0.0235 CHYp) V56tg(2) 9.68 0.0019 6.000.0143 V56tg(3) 9.46 0.0021 5.88 0.0153

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. In the claims, means-plus-function clauses, where used, areintended to cover the structures described herein as performing therecited function and not only structural equivalents but also equivalentstructures. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

That which is claimed:
 1. A method of evaluating a subject's risk ofdeveloping type 2 diabetes and/or of having prediabetes, comprising:obtaining a Nuclear Magnetic Resonance (NMR) spectrum of a biosamplecomprising blood, plasma, or serum of the subject; and programmaticallycalculating a diabetes risk index score of the subject using a model ofrisk of developing type 2 diabetes, wherein the model includes NMRderived measurements of inflammatory biomarker GlycA and a plurality ofselected lipoprotein components using lipoprotein subclasses, sizes andconcentrations obtained from the NMR spectrum of the biosample.
 2. Themethod of claim 1, further comprising programmatically defining at leasttwo different models of risk of developing type 2 diabetes, the at leasttwo different models including a model for subjects on a statin therapythat includes lipoprotein components that are statin insensitive and amodel for subjects not on a statin therapy.
 3. The method of claim 2,further comprising, before the programmatic calculation: electronicallyidentifying a valine signal as located upstream or downstream a definednumber of data points of a peak of a defined diluent in the biosample,wherein the obtained NMR spectrum comprises a valine fitting region ofthe biosample; electronically deconvolving the NMR spectrum using adefined deconvolution model; and electronically quantifying valine usingthe deconvolved NMR spectrum.
 4. The method of claim 1, furthercomprising programmatically defining at least two different models ofrisk of developing type 2 diabetes, the at least two different modelswith different lipoprotein components including a model for fastingbiosamples, and a model for non-fasting biosamples.
 5. The method ofclaim 1, wherein the model of risk includes NMR derived measurements ofvaline.
 6. The method of claim 1, further comprising: programmaticallyevaluating a fasting blood glucose measurement of the subject using thebiosample, wherein the diabetes risk index score is a numerical scorewithin a defined score range, with scores associated with a fourthquartile (4Q) or fifth quintile (5Q) of a population norm reflecting anincreased or high risk of developing type 2 diabetes within 5-7 years;and programmatically identifying respective subjects that are atincreased risk of developing type 2 diabetes prior to onset of type 2diabetes when (i) fasting blood glucose levels are between 90-110 mg/dLand (ii) the diabetes risk score is in the 4Q or 5Q range.
 7. The methodof claim 1, further comprising: programmatically evaluating a fastingblood glucose measurement of the subject, wherein the diabetes riskindex score is a numerical score within a defined score range, withscores associated with a fourth quartile (4Q) or fifth quintile (5Q) ofa population norm reflecting an increased or high risk of developingtype 2 diabetes within 5-7 years; and programmatically identifyingrespective subjects that are at increased risk of developing type 2diabetes prior to onset of type 2 diabetes when fasting blood glucose(FPG) levels are between 90-125 mg/dL, wherein the programmaticallyidentifying stratifies risk in subjects having the same FPG and adifferent diabetes risk score.
 8. The method of claim 1, wherein therisk model includes only NMR derived measurements of the subject'sblood, plasma, or serum biosample.
 9. The method of claim 1, furthercomprising, before the programmatic calculation, placing the biosamplein an NMR spectrometer; deconvolving the obtained NMR spectrum; andcalculating NMR derived measurements of GlycA and the plurality ofselected lipoprotein components based on the deconvolved NMR spectrum.10. The method of claim 9, further comprising calculating an NMR derivedmeasurement of branched chain amino acid valine.
 11. The method of claim1, further comprising programmatically generating a report thatidentifies a respective subject as at risk of developing prediabetes ifa fasting blood plasma or serum glucose value is between about 90-99mg/dl and the diabetes risk index is in a fourth quartile or fifthquintile of a population norm.
 12. The method of claim 1, wherein theselected lipoprotein components comprise at least two of the following:large VLDL subclass particle number, medium VLDL subclass particlenumber, total HDL subclass particle number, medium HDL subclass particlenumber and VLDL particle size.
 13. The method of claim 1, wherein themodel includes a ratio of medium HDL-P to total HDL-P.
 14. The method ofclaim 1, wherein the model includes VLDL subclass particle size (vsz3),a ratio of medium HDL-P to total HDL-P (HMP/HDLP) multiplied by GlycAand a ratio of VLDL size by a sum of large VLDL-P and medium VLDL-P. 15.The method of claim 1, further comprising, before the programmaticcalculation: electronically deconvolving the NMR spectrum using adefined deconvolution model with high density lipoprotein (HDL)components, low density lipoprotein (LDL) components, VLDL (very lowdensity lipoprotein)/chylomicron components, and curve fit functionsassociated with at least a GlycA peak region centered at 2.00 ppm,wherein the GlycA fitting region extends from 1.845 ppm to 2.080 andwherein a GlycA concentration is measured using the curve fit functions.16. The method of claim 15, further comprising applying a conversionfactor to the measure of GlycA to provide the measure in μmol/L.
 17. Themethod of claim 15, wherein the curve fit functions are overlappingcurve fit functions, and wherein the measure of GlycA is generated bysumming a defined number of curve fit functions, and wherein thedeconvolution model further comprises a protein signal component forprotein having a density greater than 1.21 g/L.
 18. The method of claim1, wherein the model includes a plurality of different defined models,including a model that includes lipoprotein components that areinsensitive to statin therapy, a model that includes lipoproteincomponents that are sensitive to statin therapy, a model for fastingbiosamples and a model for nonfasting biosamples.
 19. A system,comprising: an NMR spectrometer configured to acquire at least one NMRspectrum of an in vitro biosample comprising blood, plasma, or serumfrom a subject; and at least one processor in communication with the NMRspectrometer, the at least one processor configured to determine adiabetes risk index score for the biosample using the NMR spectrum basedon a model of risk to convergence to type 2 diabetes within 5-7 years,wherein the model includes NMR derived measurements of inflammatorybiomarker GlycA and a plurality of selected lipoprotein components usinglipoprotein subclasses, sizes and concentrations obtained from the NMRspectrum of the in vitro biosample.
 20. The system of claim 19, whereinthe at least one processor is configured to deconvolve the NMR spectrumand generate: (i) an NMR measurement of GlycA: (ii) an NMR measurementof valine; (iii) NMR measurements of lipoprotein parameters; and (iv)the diabetes risk index using the NMR measurements of GlycA, valine andthe lipoprotein parameters as components of the model.
 21. The system ofclaim 19, wherein the at least one processor is configured to define atleast two different models of risk of developing type 2 diabetes, the atleast two different models including a model for subjects on a statintherapy that includes lipoprotein components that are statin insensitiveand a model for subjects not on a statin therapy with at least onedifferent lipoprotein component.
 22. The system of claim 19, wherein theselected lipoprotein components comprise at least two of the following:large VLDL subclass particle number, medium VLDL subclass particlenumber, total HDL subclass particle number, medium HDL subclass particlenumber and VLDL particle size.
 23. The system of claim 19, wherein themodel includes a ratio of medium HDL-P to total HDL-P.
 24. The system ofclaim 19, wherein the model includes VLDL subclass particle size (vsz3),a ratio of medium HDL-P to total HDL-P (HMP/HDLP) multiplied by GlycAand a ratio of VLDL size by a sum of large VLDL-P and medium VLDL-P. 25.An NMR system comprising: a NMR spectrometer; and at least one processorin communication with the spectrometer configured to: (a) obtain (i) NMRsignal of a defined GlycA fitting region of NMR spectra associated withbiomarker GlycA of a blood, plasma or serum specimen; (ii) NMR signal ofa defined valine fitting region of NMR spectra associated with thespecimen; and (iii) NMR signal of lipoprotein parameters; (b) calculatemeasurements of GlycA, valine and the lipoprotein parameters; and (c)calculate a diabetes risk index using a model of risk of developing type2 diabetes and/or having prediabetes that uses the calculatedmeasurements comprising GlycA, valine and a plurality of the lipoproteinparameters, wherein the diabetes risk index score has a definednumerical range.
 26. The system of claim 25, wherein the at least oneprocessor comprises at least one local or remote processor and whereinthe at least one processor is configured to deconvolve at least onecomposite NMR spectrum of the specimen to generate a measurement ofGlycA, valine and the lipoprotein parameters.